Overview

Dataset statistics

Number of variables116
Number of observations8403
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 MiB
Average record size in memory928.0 B

Variable types

Categorical89
Numeric27

Alerts

ID has a high cardinality: 8403 distinct values High cardinality
year is highly correlated with BP16_1 and 1 other fieldsHigh correlation
sex is highly correlated with HE_ht and 2 other fieldsHigh correlation
HE_ht is highly correlated with sex and 1 other fieldsHigh correlation
HE_wt is highly correlated with HE_ht and 2 other fieldsHigh correlation
HE_BMI is highly correlated with HE_wt and 1 other fieldsHigh correlation
M_2_yr is highly correlated with M_2_rs and 34 other fieldsHigh correlation
M_2_rs is highly correlated with M_2_yr and 34 other fieldsHigh correlation
LQ_1EQL is highly correlated with LQ_2EQL and 28 other fieldsHigh correlation
LQ_2EQL is highly correlated with M_2_yr and 37 other fieldsHigh correlation
LQ_3EQL is highly correlated with LQ_1EQL and 34 other fieldsHigh correlation
LQ_4EQL is highly correlated with LQ_1EQL and 25 other fieldsHigh correlation
LQ_5EQL is highly correlated with LQ_1EQL and 33 other fieldsHigh correlation
BD2_1 is highly correlated with BD2_31High correlation
BD2_31 is highly correlated with BD2_1High correlation
BP16_1 is highly correlated with year and 1 other fieldsHigh correlation
BP16_2 is highly correlated with year and 1 other fieldsHigh correlation
BP1 is highly correlated with mh_stressHigh correlation
mh_stress is highly correlated with BP1High correlation
BS1_1 is highly correlated with sex and 2 other fieldsHigh correlation
BS3_1 is highly correlated with sex and 2 other fieldsHigh correlation
BS3_2 is highly correlated with BS1_1 and 4 other fieldsHigh correlation
BS3_3 is highly correlated with BS3_2 and 21 other fieldsHigh correlation
BS12_47 is highly correlated with BS3_2 and 21 other fieldsHigh correlation
BS9_2 is highly correlated with BS3_3 and 1 other fieldsHigh correlation
BS13 is highly correlated with BS3_3 and 1 other fieldsHigh correlation
sm_presnt is highly correlated with BS3_2High correlation
BE3_71 is highly correlated with M_2_yr and 44 other fieldsHigh correlation
BE3_72 is highly correlated with M_2_yr and 44 other fieldsHigh correlation
BE3_81 is highly correlated with M_2_yr and 43 other fieldsHigh correlation
BE3_82 is highly correlated with M_2_yr and 43 other fieldsHigh correlation
BE3_75 is highly correlated with M_2_yr and 44 other fieldsHigh correlation
BE3_76 is highly correlated with M_2_yr and 44 other fieldsHigh correlation
BE3_85 is highly correlated with M_2_yr and 34 other fieldsHigh correlation
BE3_86 is highly correlated with M_2_yr and 34 other fieldsHigh correlation
BE3_91 is highly correlated with LQ_2EQL and 26 other fieldsHigh correlation
BE8_1 is highly correlated with LQ_3EQL and 11 other fieldsHigh correlation
BE3_31 is highly correlated with BE3_71 and 17 other fieldsHigh correlation
BE3_32 is highly correlated with LQ_2EQL and 19 other fieldsHigh correlation
BE5_1 is highly correlated with M_2_yr and 31 other fieldsHigh correlation
pa_aerobic is highly correlated with BE3_91High correlation
DI1_pr is highly correlated with HE_HPHigh correlation
DI3_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DI5_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DM2_pr is highly correlated with M_2_yr and 30 other fieldsHigh correlation
DM3_pr is highly correlated with M_2_yr and 37 other fieldsHigh correlation
DM4_pr is highly correlated with M_2_yr and 31 other fieldsHigh correlation
DJ2_pr is highly correlated with M_2_yr and 38 other fieldsHigh correlation
DJ4_pr is highly correlated with M_2_yr and 37 other fieldsHigh correlation
DJ6_pr is highly correlated with M_2_yr and 39 other fieldsHigh correlation
DJ8_pr is highly correlated with M_2_yr and 38 other fieldsHigh correlation
DI6_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DF2_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DL1_pr is highly correlated with M_2_yr and 43 other fieldsHigh correlation
DE2_pr is highly correlated with M_2_yr and 40 other fieldsHigh correlation
DH4_pr is highly correlated with M_2_yr and 39 other fieldsHigh correlation
DC1_pr is highly correlated with M_2_yr and 43 other fieldsHigh correlation
DC2_pr is highly correlated with M_2_yr and 44 other fieldsHigh correlation
DC3_pr is highly correlated with M_2_yr and 43 other fieldsHigh correlation
DC4_pr is highly correlated with M_2_yr and 44 other fieldsHigh correlation
DC5_pr is highly correlated with M_2_yr and 44 other fieldsHigh correlation
DC6_pr is highly correlated with M_2_yr and 44 other fieldsHigh correlation
DC7_pr is highly correlated with M_2_yr and 44 other fieldsHigh correlation
DK8_pr is highly correlated with M_2_yr and 43 other fieldsHigh correlation
DK9_pr is highly correlated with M_2_yr and 44 other fieldsHigh correlation
DK4_pr is highly correlated with M_2_yr and 44 other fieldsHigh correlation
HE_THfh1 is highly correlated with HE_THfh2 and 19 other fieldsHigh correlation
HE_THfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_THfh3 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HBfh1 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HBfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HBfh3 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_fh is highly correlated with HE_HPfh1 and 7 other fieldsHigh correlation
HE_HPfh1 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HPfh2 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HPfh3 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HLfh1 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HLfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HLfh3 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_IHDfh1 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_IHDfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_IHDfh3 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_STRfh1 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_STRfh2 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_STRfh3 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_DMfh1 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_DMfh2 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_DMfh3 is highly correlated with HE_THfh1 and 20 other fieldsHigh correlation
HE_HP is highly correlated with DI1_prHigh correlation
HE_obe is highly correlated with HE_wt and 1 other fieldsHigh correlation
HE_hepaB is highly correlated with HE_hepaC and 1 other fieldsHigh correlation
HE_hepaC is highly correlated with HE_hepaB and 1 other fieldsHigh correlation
HE_anem is highly correlated with HE_hepaB and 1 other fieldsHigh correlation
T_NQ_OCP is highly correlated with T_Q_VNHigh correlation
T_Q_VN is highly correlated with T_NQ_OCPHigh correlation
L_BR is highly correlated with L_LN and 5 other fieldsHigh correlation
L_LN is highly correlated with L_BR and 6 other fieldsHigh correlation
L_DN is highly correlated with L_BR and 5 other fieldsHigh correlation
L_BR_FQ is highly correlated with L_BR and 5 other fieldsHigh correlation
L_LN_FQ is highly correlated with L_BR and 4 other fieldsHigh correlation
L_DN_FQ is highly correlated with L_BR and 5 other fieldsHigh correlation
L_OUT_FQ is highly correlated with L_LNHigh correlation
LS_1YR is highly correlated with L_BR and 4 other fieldsHigh correlation
HE_ht is highly correlated with HE_wt and 86 other fieldsHigh correlation
HE_wt is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_BMI is highly correlated with HE_ht and 86 other fieldsHigh correlation
M_2_yr is highly correlated with HE_ht and 92 other fieldsHigh correlation
M_2_rs is highly correlated with HE_ht and 86 other fieldsHigh correlation
LQ_1EQL is highly correlated with HE_ht and 93 other fieldsHigh correlation
LQ_2EQL is highly correlated with HE_ht and 93 other fieldsHigh correlation
LQ_3EQL is highly correlated with HE_ht and 93 other fieldsHigh correlation
LQ_4EQL is highly correlated with HE_ht and 93 other fieldsHigh correlation
LQ_5EQL is highly correlated with HE_ht and 93 other fieldsHigh correlation
BO1_1 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BO2_1 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BD1_11 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BD2_1 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BD2_31 is highly correlated with HE_ht and 92 other fieldsHigh correlation
dr_month is highly correlated with HE_ht and 88 other fieldsHigh correlation
BP16_1 is highly correlated with BP16_2High correlation
BP16_2 is highly correlated with BP16_1High correlation
BP1 is highly correlated with HE_ht and 92 other fieldsHigh correlation
mh_stress is highly correlated with HE_ht and 88 other fieldsHigh correlation
BS1_1 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BS3_1 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BS3_2 is highly correlated with M_2_yr and 51 other fieldsHigh correlation
BS3_3 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BS12_47 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BS8_2 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BS9_2 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BS13 is highly correlated with HE_ht and 92 other fieldsHigh correlation
sm_presnt is highly correlated with HE_ht and 88 other fieldsHigh correlation
BE3_71 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BE3_72 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BE3_81 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BE3_82 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BE3_75 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BE3_76 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BE3_85 is highly correlated with HE_ht and 93 other fieldsHigh correlation
BE3_86 is highly correlated with HE_ht and 92 other fieldsHigh correlation
BE3_91 is highly correlated with HE_ht and 93 other fieldsHigh correlation
BE8_1 is highly correlated with M_2_yr and 57 other fieldsHigh correlation
BE3_31 is highly correlated with M_2_yr and 58 other fieldsHigh correlation
BE3_32 is highly correlated with LQ_1EQL and 8 other fieldsHigh correlation
BE5_1 is highly correlated with HE_ht and 92 other fieldsHigh correlation
pa_aerobic is highly correlated with dr_month and 3 other fieldsHigh correlation
DI1_pr is highly correlated with HE_ht and 91 other fieldsHigh correlation
DI2_pr is highly correlated with HE_ht and 91 other fieldsHigh correlation
DI3_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DI4_pr is highly correlated with dr_month and 3 other fieldsHigh correlation
DI5_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DM2_pr is highly correlated with HE_ht and 91 other fieldsHigh correlation
DM3_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DM4_pr is highly correlated with HE_ht and 91 other fieldsHigh correlation
DJ2_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DJ4_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DJ6_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DJ8_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DI6_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DF2_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DL1_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DE1_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DE2_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DH4_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC1_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC2_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC3_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC4_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC5_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC6_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC7_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DK8_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DK9_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DK4_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
HE_Upro is highly correlated with HE_ht and 88 other fieldsHigh correlation
HE_THfh1 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_THfh2 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_THfh3 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_HBfh1 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_HBfh2 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_HBfh3 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_fh is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_HPfh1 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_HPfh2 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_HPfh3 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_HLfh1 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_HLfh2 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_HLfh3 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_IHDfh1 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_IHDfh2 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_IHDfh3 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_STRfh1 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_STRfh2 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_STRfh3 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_DMfh1 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_DMfh2 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_DMfh3 is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_HP is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_obe is highly correlated with HE_ht and 86 other fieldsHigh correlation
HE_DM_HbA1c is highly correlated with HE_HCHOL and 4 other fieldsHigh correlation
HE_HCHOL is highly correlated with HE_DM_HbA1c and 4 other fieldsHigh correlation
HE_HTG is highly correlated with HE_DM_HbA1c and 4 other fieldsHigh correlation
HE_hepaB is highly correlated with HE_wt and 85 other fieldsHigh correlation
HE_hepaC is highly correlated with HE_wt and 85 other fieldsHigh correlation
HE_anem is highly correlated with HE_wt and 85 other fieldsHigh correlation
T_NQ_OCP is highly correlated with T_Q_VNHigh correlation
T_Q_VN is highly correlated with T_NQ_OCPHigh correlation
L_BR is highly correlated with L_LN and 6 other fieldsHigh correlation
L_LN is highly correlated with L_BR and 6 other fieldsHigh correlation
L_DN is highly correlated with L_BR and 6 other fieldsHigh correlation
L_BR_FQ is highly correlated with L_BR and 6 other fieldsHigh correlation
L_LN_FQ is highly correlated with L_BR and 6 other fieldsHigh correlation
L_DN_FQ is highly correlated with L_BR and 6 other fieldsHigh correlation
L_OUT_FQ is highly correlated with L_BR and 6 other fieldsHigh correlation
LS_1YR is highly correlated with L_BR and 6 other fieldsHigh correlation
year is highly correlated with BP16_1 and 1 other fieldsHigh correlation
sex is highly correlated with HE_ht and 2 other fieldsHigh correlation
HE_ht is highly correlated with sexHigh correlation
HE_wt is highly correlated with HE_BMI and 1 other fieldsHigh correlation
HE_BMI is highly correlated with HE_wt and 1 other fieldsHigh correlation
M_2_yr is highly correlated with M_2_rs and 32 other fieldsHigh correlation
M_2_rs is highly correlated with M_2_yr and 33 other fieldsHigh correlation
LQ_1EQL is highly correlated with LQ_2EQL and 25 other fieldsHigh correlation
LQ_2EQL is highly correlated with M_2_rs and 35 other fieldsHigh correlation
LQ_3EQL is highly correlated with LQ_1EQL and 31 other fieldsHigh correlation
LQ_4EQL is highly correlated with LQ_1EQL and 21 other fieldsHigh correlation
LQ_5EQL is highly correlated with LQ_1EQL and 32 other fieldsHigh correlation
BD2_1 is highly correlated with BD2_31High correlation
BD2_31 is highly correlated with BD2_1High correlation
BP16_1 is highly correlated with year and 1 other fieldsHigh correlation
BP16_2 is highly correlated with year and 1 other fieldsHigh correlation
BP1 is highly correlated with mh_stressHigh correlation
mh_stress is highly correlated with BP1High correlation
BS1_1 is highly correlated with sex and 2 other fieldsHigh correlation
BS3_1 is highly correlated with sex and 2 other fieldsHigh correlation
BS3_2 is highly correlated with BS1_1 and 4 other fieldsHigh correlation
BS3_3 is highly correlated with BS3_2 and 18 other fieldsHigh correlation
BS12_47 is highly correlated with BS3_2 and 17 other fieldsHigh correlation
BS9_2 is highly correlated with BS3_3High correlation
BS13 is highly correlated with BS3_3 and 1 other fieldsHigh correlation
sm_presnt is highly correlated with BS3_2High correlation
BE3_71 is highly correlated with M_2_yr and 41 other fieldsHigh correlation
BE3_72 is highly correlated with M_2_yr and 41 other fieldsHigh correlation
BE3_81 is highly correlated with M_2_yr and 39 other fieldsHigh correlation
BE3_82 is highly correlated with M_2_yr and 39 other fieldsHigh correlation
BE3_75 is highly correlated with M_2_yr and 41 other fieldsHigh correlation
BE3_76 is highly correlated with M_2_yr and 41 other fieldsHigh correlation
BE3_85 is highly correlated with M_2_yr and 34 other fieldsHigh correlation
BE3_86 is highly correlated with M_2_yr and 34 other fieldsHigh correlation
BE3_91 is highly correlated with LQ_2EQL and 20 other fieldsHigh correlation
BE3_32 is highly correlated with BE3_91High correlation
BE5_1 is highly correlated with M_2_yr and 29 other fieldsHigh correlation
DI1_pr is highly correlated with HE_HPHigh correlation
DI3_pr is highly correlated with M_2_yr and 34 other fieldsHigh correlation
DI5_pr is highly correlated with M_2_yr and 38 other fieldsHigh correlation
DM2_pr is highly correlated with BE3_71 and 26 other fieldsHigh correlation
DM3_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DM4_pr is highly correlated with M_2_yr and 30 other fieldsHigh correlation
DJ2_pr is highly correlated with M_2_yr and 35 other fieldsHigh correlation
DJ4_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DJ6_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DJ8_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DI6_pr is highly correlated with M_2_yr and 36 other fieldsHigh correlation
DF2_pr is highly correlated with M_2_yr and 34 other fieldsHigh correlation
DL1_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DE2_pr is highly correlated with M_2_yr and 38 other fieldsHigh correlation
DH4_pr is highly correlated with M_2_yr and 38 other fieldsHigh correlation
DC1_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DC2_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DC3_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DC4_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DC5_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DC6_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DC7_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DK8_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DK9_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
DK4_pr is highly correlated with M_2_yr and 41 other fieldsHigh correlation
HE_THfh1 is highly correlated with HE_THfh2 and 19 other fieldsHigh correlation
HE_THfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_THfh3 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HBfh1 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HBfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HBfh3 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_fh is highly correlated with HE_HPfh1 and 6 other fieldsHigh correlation
HE_HPfh1 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HPfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HPfh3 is highly correlated with HE_THfh1 and 16 other fieldsHigh correlation
HE_HLfh1 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HLfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_HLfh3 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_IHDfh1 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_IHDfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_IHDfh3 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_STRfh1 is highly correlated with HE_THfh1 and 18 other fieldsHigh correlation
HE_STRfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_STRfh3 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_DMfh1 is highly correlated with HE_THfh1 and 18 other fieldsHigh correlation
HE_DMfh2 is highly correlated with HE_THfh1 and 19 other fieldsHigh correlation
HE_DMfh3 is highly correlated with HE_THfh1 and 17 other fieldsHigh correlation
HE_HP is highly correlated with DI1_prHigh correlation
HE_obe is highly correlated with HE_wt and 1 other fieldsHigh correlation
HE_hepaB is highly correlated with HE_hepaC and 1 other fieldsHigh correlation
HE_hepaC is highly correlated with HE_hepaB and 1 other fieldsHigh correlation
HE_anem is highly correlated with HE_hepaB and 1 other fieldsHigh correlation
T_NQ_OCP is highly correlated with T_Q_VNHigh correlation
T_Q_VN is highly correlated with T_NQ_OCPHigh correlation
L_BR is highly correlated with L_LN and 5 other fieldsHigh correlation
L_LN is highly correlated with L_BR and 4 other fieldsHigh correlation
L_DN is highly correlated with L_BR and 4 other fieldsHigh correlation
L_BR_FQ is highly correlated with L_BR and 4 other fieldsHigh correlation
L_LN_FQ is highly correlated with L_BR and 4 other fieldsHigh correlation
L_DN_FQ is highly correlated with L_BR and 5 other fieldsHigh correlation
LS_1YR is highly correlated with L_BR and 1 other fieldsHigh correlation
BS8_2 is highly correlated with LQ_4EQL and 60 other fieldsHigh correlation
HE_hepaB is highly correlated with HE_hepaC and 3 other fieldsHigh correlation
L_BR_FQ is highly correlated with L_DN_FQ and 5 other fieldsHigh correlation
L_DN_FQ is highly correlated with L_BR_FQ and 5 other fieldsHigh correlation
LQ_4EQL is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DC2_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DI3_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
LQ_1EQL is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DJ8_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
L_LN is highly correlated with L_BR_FQ and 5 other fieldsHigh correlation
mh_stress is highly correlated with BS8_2 and 66 other fieldsHigh correlation
HE_DMfh3 is highly correlated with mh_stress and 30 other fieldsHigh correlation
DM4_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DJ6_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
HE_HLfh3 is highly correlated with mh_stress and 30 other fieldsHigh correlation
HE_HLfh2 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
HE_fh is highly correlated with BS8_2 and 67 other fieldsHigh correlation
DM2_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
M_2_yr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
BS1_1 is highly correlated with BS8_2 and 61 other fieldsHigh correlation
DM3_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
L_DN is highly correlated with L_BR_FQ and 5 other fieldsHigh correlation
DF2_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DJ2_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DI1_pr is highly correlated with BS8_2 and 60 other fieldsHigh correlation
sm_presnt is highly correlated with BS8_2 and 67 other fieldsHigh correlation
DE2_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
BE3_81 is highly correlated with BS8_2 and 69 other fieldsHigh correlation
DI5_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
HE_THfh1 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
DC3_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
HE_HBfh1 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
DC6_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
HE_DMfh1 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
BO1_1 is highly correlated with BS8_2 and 60 other fieldsHigh correlation
HE_hepaC is highly correlated with HE_hepaB and 3 other fieldsHigh correlation
DK4_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
LQ_3EQL is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DC7_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DL1_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
HE_HP is highly correlated with BS8_2 and 66 other fieldsHigh correlation
DI6_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
BS13 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
DC1_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
BE3_85 is highly correlated with BS8_2 and 69 other fieldsHigh correlation
HE_IHDfh1 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
HE_HPfh2 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
LQ_5EQL is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DI2_pr is highly correlated with BS8_2 and 61 other fieldsHigh correlation
DC5_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
HE_anem is highly correlated with HE_hepaB and 3 other fieldsHigh correlation
HE_HPfh3 is highly correlated with mh_stress and 30 other fieldsHigh correlation
DH4_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DJ4_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
LQ_2EQL is highly correlated with BS8_2 and 62 other fieldsHigh correlation
BS9_2 is highly correlated with BS8_2 and 60 other fieldsHigh correlation
HE_IHDfh3 is highly correlated with mh_stress and 30 other fieldsHigh correlation
HE_DM_HbA1c is highly correlated with HE_hepaB and 5 other fieldsHigh correlation
DE1_pr is highly correlated with BS8_2 and 61 other fieldsHigh correlation
T_Q_VN is highly correlated with T_NQ_OCPHigh correlation
HE_HBfh2 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
HE_THfh3 is highly correlated with mh_stress and 30 other fieldsHigh correlation
HE_HTG is highly correlated with HE_DM_HbA1c and 1 other fieldsHigh correlation
BE3_91 is highly correlated with BS8_2 and 69 other fieldsHigh correlation
HE_HBfh3 is highly correlated with mh_stress and 30 other fieldsHigh correlation
L_LN_FQ is highly correlated with L_BR_FQ and 5 other fieldsHigh correlation
DK8_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
DC4_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
L_BR is highly correlated with L_BR_FQ and 5 other fieldsHigh correlation
HE_THfh2 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
HE_DMfh2 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
DK9_pr is highly correlated with BS8_2 and 62 other fieldsHigh correlation
LS_1YR is highly correlated with L_BR_FQ and 5 other fieldsHigh correlation
HE_STRfh3 is highly correlated with mh_stress and 30 other fieldsHigh correlation
BE3_71 is highly correlated with BS8_2 and 69 other fieldsHigh correlation
HE_IHDfh2 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
dr_month is highly correlated with BS8_2 and 67 other fieldsHigh correlation
sex is highly correlated with BS1_1High correlation
HE_STRfh1 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
T_NQ_OCP is highly correlated with T_Q_VNHigh correlation
pa_aerobic is highly correlated with LQ_4EQL and 35 other fieldsHigh correlation
HE_HLfh1 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
BE3_75 is highly correlated with BS8_2 and 69 other fieldsHigh correlation
DI4_pr is highly correlated with LQ_4EQL and 35 other fieldsHigh correlation
HE_STRfh2 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
HE_HPfh1 is highly correlated with BS8_2 and 67 other fieldsHigh correlation
HE_HCHOL is highly correlated with HE_hepaB and 5 other fieldsHigh correlation
year is highly correlated with T_Q_VNHigh correlation
sex is highly correlated with BS1_1High correlation
HE_ht is highly correlated with HE_wt and 93 other fieldsHigh correlation
HE_wt is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_BMI is highly correlated with HE_ht and 89 other fieldsHigh correlation
M_2_yr is highly correlated with HE_ht and 92 other fieldsHigh correlation
M_2_rs is highly correlated with HE_ht and 94 other fieldsHigh correlation
LQ_1EQL is highly correlated with HE_ht and 92 other fieldsHigh correlation
LQ_2EQL is highly correlated with HE_ht and 92 other fieldsHigh correlation
LQ_3EQL is highly correlated with HE_ht and 92 other fieldsHigh correlation
LQ_4EQL is highly correlated with HE_ht and 92 other fieldsHigh correlation
LQ_5EQL is highly correlated with HE_ht and 92 other fieldsHigh correlation
BO1_1 is highly correlated with HE_ht and 91 other fieldsHigh correlation
BO2_1 is highly correlated with HE_ht and 90 other fieldsHigh correlation
BD1_11 is highly correlated with HE_ht and 90 other fieldsHigh correlation
BD2_1 is highly correlated with HE_ht and 90 other fieldsHigh correlation
BD2_31 is highly correlated with HE_ht and 90 other fieldsHigh correlation
dr_month is highly correlated with HE_ht and 81 other fieldsHigh correlation
BP16_1 is highly correlated with BP16_2 and 7 other fieldsHigh correlation
BP16_2 is highly correlated with BP16_1 and 2 other fieldsHigh correlation
BP1 is highly correlated with HE_ht and 90 other fieldsHigh correlation
mh_stress is highly correlated with HE_ht and 80 other fieldsHigh correlation
BS1_1 is highly correlated with sex and 92 other fieldsHigh correlation
BS3_1 is highly correlated with HE_ht and 90 other fieldsHigh correlation
BS3_2 is highly correlated with HE_BMI and 46 other fieldsHigh correlation
BS3_3 is highly correlated with HE_ht and 93 other fieldsHigh correlation
BS12_47 is highly correlated with HE_ht and 90 other fieldsHigh correlation
BS8_2 is highly correlated with HE_ht and 91 other fieldsHigh correlation
BS9_2 is highly correlated with HE_ht and 91 other fieldsHigh correlation
BS13 is highly correlated with HE_ht and 93 other fieldsHigh correlation
sm_presnt is highly correlated with HE_ht and 81 other fieldsHigh correlation
BE3_71 is highly correlated with HE_ht and 95 other fieldsHigh correlation
BE3_72 is highly correlated with HE_ht and 90 other fieldsHigh correlation
BE3_81 is highly correlated with HE_ht and 95 other fieldsHigh correlation
BE3_82 is highly correlated with HE_ht and 90 other fieldsHigh correlation
BE3_75 is highly correlated with HE_ht and 95 other fieldsHigh correlation
BE3_76 is highly correlated with HE_ht and 90 other fieldsHigh correlation
BE3_85 is highly correlated with HE_ht and 95 other fieldsHigh correlation
BE3_86 is highly correlated with HE_ht and 90 other fieldsHigh correlation
BE3_91 is highly correlated with HE_ht and 95 other fieldsHigh correlation
BE8_1 is highly correlated with HE_ht and 95 other fieldsHigh correlation
BE3_31 is highly correlated with HE_ht and 96 other fieldsHigh correlation
BE3_32 is highly correlated with HE_ht and 94 other fieldsHigh correlation
BE5_1 is highly correlated with HE_ht and 90 other fieldsHigh correlation
pa_aerobic is highly correlated with HE_ht and 48 other fieldsHigh correlation
DI1_pr is highly correlated with HE_ht and 89 other fieldsHigh correlation
DI2_pr is highly correlated with HE_ht and 90 other fieldsHigh correlation
DI3_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DI4_pr is highly correlated with HE_BMI and 72 other fieldsHigh correlation
DI5_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DM2_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DM3_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DM4_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DJ2_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DJ4_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DJ6_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DJ8_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DI6_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DF2_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DL1_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DE1_pr is highly correlated with HE_ht and 90 other fieldsHigh correlation
DE2_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DH4_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC1_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC2_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC3_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC4_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC5_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC6_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DC7_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
DK8_pr is highly correlated with HE_ht and 93 other fieldsHigh correlation
DK9_pr is highly correlated with HE_ht and 93 other fieldsHigh correlation
DK4_pr is highly correlated with HE_ht and 92 other fieldsHigh correlation
HE_Upro is highly correlated with HE_BMI and 74 other fieldsHigh correlation
HE_THfh1 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_THfh2 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_THfh3 is highly correlated with HE_ht and 88 other fieldsHigh correlation
HE_HBfh1 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_HBfh2 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_HBfh3 is highly correlated with HE_ht and 88 other fieldsHigh correlation
HE_fh is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_HPfh1 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_HPfh2 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_HPfh3 is highly correlated with HE_ht and 88 other fieldsHigh correlation
HE_HLfh1 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_HLfh2 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_HLfh3 is highly correlated with HE_ht and 88 other fieldsHigh correlation
HE_IHDfh1 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_IHDfh2 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_IHDfh3 is highly correlated with HE_ht and 88 other fieldsHigh correlation
HE_STRfh1 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_STRfh2 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_STRfh3 is highly correlated with HE_ht and 88 other fieldsHigh correlation
HE_DMfh1 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_DMfh2 is highly correlated with HE_ht and 93 other fieldsHigh correlation
HE_DMfh3 is highly correlated with HE_ht and 88 other fieldsHigh correlation
HE_HP is highly correlated with HE_ht and 92 other fieldsHigh correlation
HE_obe is highly correlated with HE_ht and 89 other fieldsHigh correlation
HE_DM_HbA1c is highly correlated with HE_BMI and 44 other fieldsHigh correlation
HE_HCHOL is highly correlated with HE_ht and 13 other fieldsHigh correlation
HE_HTG is highly correlated with M_2_rs and 10 other fieldsHigh correlation
HE_hepaB is highly correlated with HE_ht and 13 other fieldsHigh correlation
HE_hepaC is highly correlated with HE_ht and 13 other fieldsHigh correlation
HE_anem is highly correlated with HE_ht and 12 other fieldsHigh correlation
T_NQ_OCP is highly correlated with BO2_1 and 36 other fieldsHigh correlation
T_Q_VN is highly correlated with year and 39 other fieldsHigh correlation
L_BR is highly correlated with L_LN and 6 other fieldsHigh correlation
L_LN is highly correlated with L_BR and 6 other fieldsHigh correlation
L_DN is highly correlated with L_BR and 6 other fieldsHigh correlation
L_BR_FQ is highly correlated with L_BR and 6 other fieldsHigh correlation
L_LN_FQ is highly correlated with L_BR and 6 other fieldsHigh correlation
L_DN_FQ is highly correlated with L_BR and 6 other fieldsHigh correlation
L_OUT_FQ is highly correlated with L_BR and 6 other fieldsHigh correlation
LS_1YR is highly correlated with L_BR and 6 other fieldsHigh correlation
ID is uniformly distributed Uniform
ID has unique values Unique
BE3_32 has 2858 (34.0%) zeros Zeros
HE_Upro has 6561 (78.1%) zeros Zeros

Reproduction

Analysis started2022-04-28 03:38:47.213338
Analysis finished2022-04-28 03:42:00.961213
Duration3 minutes and 13.75 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct8403
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
b'E756235701'
 
1
b'A612237401'
 
1
b'C751247602'
 
1
b'N653268001'
 
1
b'C752361501'
 
1
Other values (8398)
8398 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8403 ?
Unique (%)100.0%

Sample

1st rowb'A651228902'
2nd rowb'A651417601'
3rd rowb'A652228901'
4th rowb'A653184701'
5th rowb'A653235705'

Common Values

ValueCountFrequency (%)
b'E756235701'1
 
< 0.1%
b'A612237401'1
 
< 0.1%
b'C751247602'1
 
< 0.1%
b'N653268001'1
 
< 0.1%
b'C752361501'1
 
< 0.1%
b'H809327501'1
 
< 0.1%
b'N805361503'1
 
< 0.1%
b'H804322401'1
 
< 0.1%
b'N601186402'1
 
< 0.1%
b'E652339402'1
 
< 0.1%
Other values (8393)8393
99.9%

Length

2022-04-28T12:42:01.077734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b'c6073428011
 
< 0.1%
b'a7140238021
 
< 0.1%
b'b6522476021
 
< 0.1%
b'm6073683021
 
< 0.1%
b'a7300291021
 
< 0.1%
b'o6591949011
 
< 0.1%
b'n6583666021
 
< 0.1%
b'a6044159021
 
< 0.1%
b'n7040271021
 
< 0.1%
b'b7582731011
 
< 0.1%
Other values (8393)8393
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2019
1735 
2020
1712 
2017
1671 
2018
1653 
2016
1632 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
20191735
20.6%
20201712
20.4%
20171671
19.9%
20181653
19.7%
20161632
19.4%

Length

2022-04-28T12:42:01.208037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:01.276710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
20191735
20.6%
20201712
20.4%
20171671
19.9%
20181653
19.7%
20161632
19.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sex
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
4785 
1
3618 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
24785
56.9%
13618
43.1%

Length

2022-04-28T12:42:02.008676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:02.225015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
24785
56.9%
13618
43.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.0092824
Minimum65
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.8 KiB
2022-04-28T12:42:02.337485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile65
Q169
median73
Q378
95-th percentile80
Maximum80
Range15
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.108991518
Coefficient of variation (CV)0.06997728714
Kurtosis-1.35643342
Mean73.0092824
Median Absolute Deviation (MAD)5
Skewness-0.004627055643
Sum613497
Variance26.10179433
MonotonicityNot monotonic
2022-04-28T12:42:02.504384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
801604
19.1%
65588
 
7.0%
69528
 
6.3%
66525
 
6.2%
70503
 
6.0%
68491
 
5.8%
67491
 
5.8%
71473
 
5.6%
72469
 
5.6%
73451
 
5.4%
Other values (6)2280
27.1%
ValueCountFrequency (%)
65588
7.0%
66525
6.2%
67491
5.8%
68491
5.8%
69528
6.3%
70503
6.0%
71473
5.6%
72469
5.6%
73451
5.4%
74406
4.8%
ValueCountFrequency (%)
801604
19.1%
79302
 
3.6%
78352
 
4.2%
77435
 
5.2%
76378
 
4.5%
75407
 
4.8%
74406
 
4.8%
73451
 
5.4%
72469
 
5.6%
71473
 
5.6%

HE_ht
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.6961799
Minimum-99
Maximum184
Zeros0
Zeros (%)0.0%
Negative354
Negative (%)4.2%
Memory size65.8 KiB
2022-04-28T12:42:02.659314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile138
Q1150
median156
Q3164
95-th percentile172
Maximum184
Range283
Interquartile range (IQR)14

Descriptive statistics

Standard deviation52.25791529
Coefficient of variation (CV)0.3562322844
Kurtosis17.61242709
Mean146.6961799
Median Absolute Deviation (MAD)7
Skewness-4.353784885
Sum1232688
Variance2730.88971
MonotonicityNot monotonic
2022-04-28T12:42:02.827412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155374
 
4.5%
-99354
 
4.2%
153332
 
4.0%
154331
 
3.9%
151326
 
3.9%
149315
 
3.7%
152307
 
3.7%
150304
 
3.6%
156302
 
3.6%
160287
 
3.4%
Other values (47)5171
61.5%
ValueCountFrequency (%)
-99354
4.2%
1282
 
< 0.1%
1303
 
< 0.1%
1312
 
< 0.1%
1323
 
< 0.1%
1332
 
< 0.1%
1349
 
0.1%
1355
 
0.1%
1368
 
0.1%
13714
 
0.2%
ValueCountFrequency (%)
1841
 
< 0.1%
1833
 
< 0.1%
1823
 
< 0.1%
1814
 
< 0.1%
18013
 
0.2%
17912
 
0.1%
17826
 
0.3%
17731
0.4%
17633
0.4%
17570
0.8%

HE_wt
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.13828395
Minimum-99
Maximum105
Zeros0
Zeros (%)0.0%
Negative245
Negative (%)2.9%
Memory size65.8 KiB
2022-04-28T12:42:02.999450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile41
Q152
median59
Q366
95-th percentile77
Maximum105
Range204
Interquartile range (IQR)14

Descriptive statistics

Standard deviation28.5098431
Coefficient of variation (CV)0.5170607618
Kurtosis22.00841212
Mean55.13828395
Median Absolute Deviation (MAD)7
Skewness-4.532253678
Sum463327
Variance812.8111538
MonotonicityNot monotonic
2022-04-28T12:42:03.141814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61357
 
4.2%
58339
 
4.0%
54334
 
4.0%
55321
 
3.8%
60307
 
3.7%
62306
 
3.6%
56301
 
3.6%
57299
 
3.6%
64295
 
3.5%
63287
 
3.4%
Other values (63)5257
62.6%
ValueCountFrequency (%)
-99245
2.9%
312
 
< 0.1%
323
 
< 0.1%
335
 
0.1%
347
 
0.1%
3516
 
0.2%
3611
 
0.1%
3720
 
0.2%
3826
 
0.3%
3928
 
0.3%
ValueCountFrequency (%)
1051
 
< 0.1%
1041
 
< 0.1%
1022
 
< 0.1%
991
 
< 0.1%
982
 
< 0.1%
971
 
< 0.1%
962
 
< 0.1%
951
 
< 0.1%
941
 
< 0.1%
936
0.1%

HE_BMI
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.43674878
Minimum-99
Maximum41
Zeros0
Zeros (%)0.0%
Negative355
Negative (%)4.2%
Memory size65.8 KiB
2022-04-28T12:42:03.274501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile16
Q121
median23
Q325
95-th percentile29
Maximum41
Range140
Interquartile range (IQR)4

Descriptive statistics

Standard deviation24.86519507
Coefficient of variation (CV)1.3486757
Kurtosis18.04455285
Mean18.43674878
Median Absolute Deviation (MAD)2
Skewness-4.432891109
Sum154924
Variance618.2779257
MonotonicityNot monotonic
2022-04-28T12:42:03.392928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
241047
12.5%
231025
12.2%
22994
11.8%
25939
11.2%
21753
9.0%
26698
8.3%
20523
6.2%
27491
5.8%
19370
 
4.4%
-99355
 
4.2%
Other values (20)1208
14.4%
ValueCountFrequency (%)
-99355
4.2%
111
 
< 0.1%
131
 
< 0.1%
145
 
0.1%
1522
 
0.3%
1646
 
0.5%
1780
 
1.0%
18203
 
2.4%
19370
4.4%
20523
6.2%
ValueCountFrequency (%)
411
 
< 0.1%
391
 
< 0.1%
383
 
< 0.1%
372
 
< 0.1%
369
 
0.1%
3513
 
0.2%
3414
 
0.2%
3327
 
0.3%
3261
0.7%
3170
0.8%

M_2_yr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
6619 
1
732 
9
698 
-99
 
231
3
 
123

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
26619
78.8%
1732
 
8.7%
9698
 
8.3%
-99231
 
2.7%
3123
 
1.5%

Length

2022-04-28T12:42:03.545998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:03.770550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
26619
78.8%
1732
 
8.7%
9698
 
8.3%
99231
 
2.7%
3123
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

M_2_rs
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.38795668
Minimum-99
Maximum99
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:03.852787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile2
Q188
median88
Q388
95-th percentile99
Maximum99
Range198
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38.26498904
Coefficient of variation (CV)0.5009296059
Kurtosis10.33612592
Mean76.38795668
Median Absolute Deviation (MAD)0
Skewness-3.221380133
Sum641888
Variance1464.209386
MonotonicityNot monotonic
2022-04-28T12:42:03.976209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
886742
80.2%
99699
 
8.3%
3249
 
3.0%
-99231
 
2.7%
2174
 
2.1%
1139
 
1.7%
464
 
0.8%
853
 
0.6%
742
 
0.5%
58
 
0.1%
ValueCountFrequency (%)
-99231
 
2.7%
1139
 
1.7%
2174
 
2.1%
3249
 
3.0%
464
 
0.8%
58
 
0.1%
62
 
< 0.1%
742
 
0.5%
853
 
0.6%
886742
80.2%
ValueCountFrequency (%)
99699
 
8.3%
886742
80.2%
853
 
0.6%
742
 
0.5%
62
 
< 0.1%
58
 
0.1%
464
 
0.8%
3249
 
3.0%
2174
 
2.1%
1139
 
1.7%

LQ_1EQL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
4701 
2
2629 
9
715 
-99
 
231
3
 
127

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
14701
55.9%
22629
31.3%
9715
 
8.5%
-99231
 
2.7%
3127
 
1.5%

Length

2022-04-28T12:42:04.098829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:04.178388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14701
55.9%
22629
31.3%
9715
 
8.5%
99231
 
2.7%
3127
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LQ_2EQL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
6677 
2
718 
9
715 
-99
 
231
3
 
62

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
16677
79.5%
2718
 
8.5%
9715
 
8.5%
-99231
 
2.7%
362
 
0.7%

Length

2022-04-28T12:42:04.300648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:04.388998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
16677
79.5%
2718
 
8.5%
9715
 
8.5%
99231
 
2.7%
362
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LQ_3EQL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
6027 
2
1322 
9
716 
-99
 
231
3
 
107

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
16027
71.7%
21322
 
15.7%
9716
 
8.5%
-99231
 
2.7%
3107
 
1.3%

Length

2022-04-28T12:42:04.504065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:04.585215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
16027
71.7%
21322
 
15.7%
9716
 
8.5%
99231
 
2.7%
3107
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LQ_4EQL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
4757 
2
2273 
9
721 
3
 
421
-99
 
231

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row3
5th row2

Common Values

ValueCountFrequency (%)
14757
56.6%
22273
27.0%
9721
 
8.6%
3421
 
5.0%
-99231
 
2.7%

Length

2022-04-28T12:42:04.684180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:04.768631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14757
56.6%
22273
27.0%
9721
 
8.6%
3421
 
5.0%
99231
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LQ_5EQL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
6366 
2
980 
9
723 
-99
 
231
3
 
103

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
16366
75.8%
2980
 
11.7%
9723
 
8.6%
-99231
 
2.7%
3103
 
1.2%

Length

2022-04-28T12:42:04.863544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:04.943777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
16366
75.8%
2980
 
11.7%
9723
 
8.6%
99231
 
2.7%
3103
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BO1_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
5911 
2
1206 
3
880 
-99
 
231
9
 
175

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row9
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
15911
70.3%
21206
 
14.4%
3880
 
10.5%
-99231
 
2.7%
9175
 
2.1%

Length

2022-04-28T12:42:05.039707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:05.120052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
15911
70.3%
21206
 
14.4%
3880
 
10.5%
99231
 
2.7%
9175
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BO2_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1435201714
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:05.219000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1
Q11
median4
Q34
95-th percentile4
Maximum9
Range108
Interquartile range (IQR)3

Descriptive statistics

Standard deviation16.73774822
Coefficient of variation (CV)116.6229671
Kurtosis30.87129501
Mean0.1435201714
Median Absolute Deviation (MAD)1
Skewness-5.706035424
Sum1206
Variance280.1522155
MonotonicityNot monotonic
2022-04-28T12:42:05.335117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
44066
48.4%
12040
24.3%
21345
 
16.0%
3568
 
6.8%
-99231
 
2.7%
9153
 
1.8%
ValueCountFrequency (%)
-99231
 
2.7%
12040
24.3%
21345
 
16.0%
3568
 
6.8%
44066
48.4%
9153
 
1.8%
ValueCountFrequency (%)
9153
 
1.8%
44066
48.4%
3568
 
6.8%
21345
 
16.0%
12040
24.3%
-99231
 
2.7%

BD1_11
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.409972629
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:05.431366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1
Q11
median4
Q38
95-th percentile8
Maximum9
Range108
Interquartile range (IQR)7

Descriptive statistics

Standard deviation17.10460642
Coefficient of variation (CV)12.13116203
Kurtosis29.67913573
Mean1.409972629
Median Absolute Deviation (MAD)3
Skewness-5.544795895
Sum11848
Variance292.5675606
MonotonicityNot monotonic
2022-04-28T12:42:05.531325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
82084
24.8%
12017
24.0%
21191
14.2%
4929
11.1%
5672
 
8.0%
6589
 
7.0%
3529
 
6.3%
-99231
 
2.7%
9161
 
1.9%
ValueCountFrequency (%)
-99231
 
2.7%
12017
24.0%
21191
14.2%
3529
 
6.3%
4929
11.1%
5672
 
8.0%
6589
 
7.0%
82084
24.8%
9161
 
1.9%
ValueCountFrequency (%)
9161
 
1.9%
82084
24.8%
6589
 
7.0%
5672
 
8.0%
4929
11.1%
3529
 
6.3%
21191
14.2%
12017
24.0%
-99231
 
2.7%

BD2_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.222420564
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:05.627558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1
Q11
median8
Q38
95-th percentile8
Maximum9
Range108
Interquartile range (IQR)7

Descriptive statistics

Standard deviation17.30782294
Coefficient of variation (CV)7.787825231
Kurtosis29.19218158
Mean2.222420564
Median Absolute Deviation (MAD)1
Skewness-5.481556593
Sum18675
Variance299.5607351
MonotonicityNot monotonic
2022-04-28T12:42:05.724735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
84101
48.8%
12093
24.9%
2931
 
11.1%
4378
 
4.5%
3366
 
4.4%
-99231
 
2.7%
9164
 
2.0%
5139
 
1.7%
ValueCountFrequency (%)
-99231
 
2.7%
12093
24.9%
2931
 
11.1%
3366
 
4.4%
4378
 
4.5%
5139
 
1.7%
84101
48.8%
9164
 
2.0%
ValueCountFrequency (%)
9164
 
2.0%
84101
48.8%
5139
 
1.7%
4378
 
4.5%
3366
 
4.4%
2931
 
11.1%
12093
24.9%
-99231
 
2.7%

BD2_31
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.249196715
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:05.844491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1
Q11
median8
Q38
95-th percentile8
Maximum9
Range108
Interquartile range (IQR)7

Descriptive statistics

Standard deviation17.31296206
Coefficient of variation (CV)7.697397894
Kurtosis29.18790072
Mean2.249196715
Median Absolute Deviation (MAD)1
Skewness-5.48125259
Sum18900
Variance299.7386553
MonotonicityNot monotonic
2022-04-28T12:42:05.940521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
84101
48.8%
12352
28.0%
2479
 
5.7%
4441
 
5.2%
3386
 
4.6%
5247
 
2.9%
-99231
 
2.7%
9166
 
2.0%
ValueCountFrequency (%)
-99231
 
2.7%
12352
28.0%
2479
 
5.7%
3386
 
4.6%
4441
 
5.2%
5247
 
2.9%
84101
48.8%
9166
 
2.0%
ValueCountFrequency (%)
9166
 
2.0%
84101
48.8%
5247
 
2.9%
4441
 
5.2%
3386
 
4.6%
2479
 
5.7%
12352
28.0%
-99231
 
2.7%

dr_month
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
5292 
1
2719 
-99
 
392

Length

Max length3
Median length1
Mean length1.093300012
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row-99
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
05292
63.0%
12719
32.4%
-99392
 
4.7%

Length

2022-04-28T12:42:06.055959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:06.157927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05292
63.0%
12719
32.4%
99392
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BP16_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct83
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean491.0330834
Minimum-99
Maximum9999
Zeros0
Zeros (%)0.0%
Negative325
Negative (%)3.9%
Memory size65.8 KiB
2022-04-28T12:42:06.251516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile3
Q17
median330
Q3450
95-th percentile600
Maximum9999
Range10098
Interquartile range (IQR)443

Descriptive statistics

Standard deviation1571.715203
Coefficient of variation (CV)3.200833621
Kurtosis31.93562129
Mean491.0330834
Median Absolute Deviation (MAD)240
Skewness5.757713276
Sum4126151
Variance2470288.678
MonotonicityNot monotonic
2022-04-28T12:42:06.425192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
420816
 
9.7%
480766
 
9.1%
7718
 
8.5%
6626
 
7.4%
9596
 
7.1%
360538
 
6.4%
8521
 
6.2%
5494
 
5.9%
540391
 
4.7%
450369
 
4.4%
Other values (73)2568
30.6%
ValueCountFrequency (%)
-99325
3.9%
13
 
< 0.1%
215
 
0.2%
379
 
0.9%
4214
 
2.5%
5494
5.9%
6626
7.4%
7718
8.5%
8521
6.2%
9596
7.1%
ValueCountFrequency (%)
9999219
2.6%
8401
 
< 0.1%
7802
 
< 0.1%
7209
 
0.1%
7051
 
< 0.1%
7001
 
< 0.1%
6905
 
0.1%
6701
 
< 0.1%
66051
 
0.6%
63029
 
0.3%

BP16_2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct84
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.2515768
Minimum-99
Maximum9999
Zeros0
Zeros (%)0.0%
Negative324
Negative (%)3.9%
Memory size65.8 KiB
2022-04-28T12:42:06.567306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile4
Q17
median330
Q3480
95-th percentile600
Maximum9999
Range10098
Interquartile range (IQR)473

Descriptive statistics

Standard deviation1585.222719
Coefficient of variation (CV)3.16885102
Kurtosis31.2437569
Mean500.2515768
Median Absolute Deviation (MAD)270
Skewness5.697138926
Sum4203614
Variance2512931.07
MonotonicityNot monotonic
2022-04-28T12:42:06.850572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480825
 
9.8%
420802
 
9.5%
7720
 
8.6%
9610
 
7.3%
6600
 
7.1%
8542
 
6.5%
360499
 
5.9%
5468
 
5.6%
540439
 
5.2%
450329
 
3.9%
Other values (74)2569
30.6%
ValueCountFrequency (%)
-99324
3.9%
13
 
< 0.1%
215
 
0.2%
371
 
0.8%
4207
 
2.5%
5468
5.6%
6600
7.1%
7720
8.6%
8542
6.5%
9610
7.3%
ValueCountFrequency (%)
9999223
2.7%
11401
 
< 0.1%
8401
 
< 0.1%
7805
 
0.1%
7501
 
< 0.1%
72017
 
0.2%
7051
 
< 0.1%
7001
 
< 0.1%
6909
 
0.1%
6701
 
< 0.1%

BP1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4046173985
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:07.003616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1
Q13
median3
Q34
95-th percentile4
Maximum9
Range108
Interquartile range (IQR)1

Descriptive statistics

Standard deviation16.75382302
Coefficient of variation (CV)41.40658083
Kurtosis31.09790404
Mean0.4046173985
Median Absolute Deviation (MAD)1
Skewness-5.736138952
Sum3400
Variance280.6905857
MonotonicityNot monotonic
2022-04-28T12:42:07.095458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
34073
48.5%
42419
28.8%
21182
 
14.1%
1309
 
3.7%
-99231
 
2.7%
9189
 
2.2%
ValueCountFrequency (%)
-99231
 
2.7%
1309
 
3.7%
21182
 
14.1%
34073
48.5%
42419
28.8%
9189
 
2.2%
ValueCountFrequency (%)
9189
 
2.2%
42419
28.8%
34073
48.5%
21182
 
14.1%
1309
 
3.7%
-99231
 
2.7%

mh_stress
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6492 
1
1491 
-99
 
420

Length

Max length3
Median length1
Mean length1.099964298
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row-99
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
06492
77.3%
11491
 
17.7%
-99420
 
5.0%

Length

2022-04-28T12:42:07.208452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:07.287744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06492
77.3%
11491
 
17.7%
99420
 
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BS1_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
3
4996 
2
2940 
-99
 
231
9
 
175
1
 
61

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row9
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
34996
59.5%
22940
35.0%
-99231
 
2.7%
9175
 
2.1%
161
 
0.7%

Length

2022-04-28T12:42:07.397193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:07.506376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
34996
59.5%
22940
35.0%
99231
 
2.7%
9175
 
2.1%
161
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BS3_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.126859455
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:07.607285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1
Q13
median8
Q38
95-th percentile8
Maximum9
Range108
Interquartile range (IQR)5

Descriptive statistics

Standard deviation17.37750603
Coefficient of variation (CV)5.557495077
Kurtosis29.82373127
Mean3.126859455
Median Absolute Deviation (MAD)0
Skewness-5.568224848
Sum26275
Variance301.9777158
MonotonicityNot monotonic
2022-04-28T12:42:07.721948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
84996
59.5%
32256
26.8%
1671
 
8.0%
-99231
 
2.7%
9177
 
2.1%
272
 
0.9%
ValueCountFrequency (%)
-99231
 
2.7%
1671
 
8.0%
272
 
0.9%
32256
26.8%
84996
59.5%
9177
 
2.1%
ValueCountFrequency (%)
9177
 
2.1%
84996
59.5%
32256
26.8%
272
 
0.9%
1671
 
8.0%
-99231
 
2.7%

BS3_2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean785.7352136
Minimum-99
Maximum999
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:07.830839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile7
Q1888
median888
Q3888
95-th percentile888
Maximum999
Range1098
Interquartile range (IQR)0

Descriptive statistics

Standard deviation290.639371
Coefficient of variation (CV)0.3698948017
Kurtosis3.831313895
Mean785.7352136
Median Absolute Deviation (MAD)0
Skewness-2.400038107
Sum6602533
Variance84471.24397
MonotonicityNot monotonic
2022-04-28T12:42:07.938426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8887252
86.3%
-99231
 
2.7%
10189
 
2.2%
999177
 
2.1%
20174
 
2.1%
561
 
0.7%
1561
 
0.7%
756
 
0.7%
340
 
0.5%
629
 
0.3%
Other values (16)133
 
1.6%
ValueCountFrequency (%)
-99231
2.7%
114
 
0.2%
222
 
0.3%
340
 
0.5%
420
 
0.2%
561
 
0.7%
629
 
0.3%
756
 
0.7%
85
 
0.1%
94
 
< 0.1%
ValueCountFrequency (%)
999177
 
2.1%
8887252
86.3%
402
 
< 0.1%
3016
 
0.2%
261
 
< 0.1%
255
 
0.1%
20174
 
2.1%
182
 
< 0.1%
176
 
0.1%
163
 
< 0.1%

BS3_3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.43056051
Minimum-99
Maximum99
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:08.070862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile88
Q188
median88
Q388
95-th percentile88
Maximum99
Range198
Interquartile range (IQR)0

Descriptive statistics

Standard deviation31.37410891
Coefficient of variation (CV)0.3806125873
Kurtosis28.01515933
Mean82.43056051
Median Absolute Deviation (MAD)0
Skewness-5.414361181
Sum692664
Variance984.3347098
MonotonicityNot monotonic
2022-04-28T12:42:08.209820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
887923
94.3%
-99231
 
2.7%
99177
 
2.1%
2017
 
0.2%
1011
 
0.1%
510
 
0.1%
77
 
0.1%
26
 
0.1%
63
 
< 0.1%
43
 
< 0.1%
Other values (10)15
 
0.2%
ValueCountFrequency (%)
-99231
2.7%
12
 
< 0.1%
26
 
0.1%
33
 
< 0.1%
43
 
< 0.1%
510
 
0.1%
63
 
< 0.1%
77
 
0.1%
91
 
< 0.1%
1011
 
0.1%
ValueCountFrequency (%)
99177
 
2.1%
887923
94.3%
301
 
< 0.1%
252
 
< 0.1%
241
 
< 0.1%
2017
 
0.2%
181
 
< 0.1%
151
 
< 0.1%
141
 
< 0.1%
122
 
< 0.1%

BS12_47
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.011424491
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:08.344341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.50049104
Coefficient of variation (CV)3.492119072
Kurtosis31.32891731
Mean5.011424491
Median Absolute Deviation (MAD)0
Skewness-5.768442932
Sum42111
Variance306.2671868
MonotonicityNot monotonic
2022-04-28T12:42:08.440217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
87899
94.0%
-99231
 
2.7%
9176
 
2.1%
255
 
0.7%
326
 
0.3%
116
 
0.2%
ValueCountFrequency (%)
-99231
 
2.7%
116
 
0.2%
255
 
0.7%
326
 
0.3%
87899
94.0%
9176
 
2.1%
ValueCountFrequency (%)
9176
 
2.1%
87899
94.0%
326
 
0.3%
255
 
0.7%
116
 
0.2%
-99231
 
2.7%

BS8_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
5282 
2
2496 
-99
 
231
1
 
216
9
 
178

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row9
3rd row2
4th row8
5th row8

Common Values

ValueCountFrequency (%)
85282
62.9%
22496
29.7%
-99231
 
2.7%
1216
 
2.6%
9178
 
2.1%

Length

2022-04-28T12:42:08.583235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:08.663588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
85282
62.9%
22496
29.7%
99231
 
2.7%
1216
 
2.6%
9178
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BS9_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
3
6954 
2
823 
-99
 
231
1
 
221
9
 
174

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row9
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
36954
82.8%
2823
 
9.8%
-99231
 
2.7%
1221
 
2.6%
9174
 
2.1%

Length

2022-04-28T12:42:08.759276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:08.856709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
36954
82.8%
2823
 
9.8%
99231
 
2.7%
1221
 
2.6%
9174
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BS13
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
7347 
1
 
642
-99
 
231
9
 
183

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row9
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
27347
87.4%
1642
 
7.6%
-99231
 
2.7%
9183
 
2.2%

Length

2022-04-28T12:42:08.985928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:09.067277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
27347
87.4%
1642
 
7.6%
99231
 
2.7%
9183
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sm_presnt
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
7254 
1
741 
-99
 
408

Length

Max length3
Median length1
Mean length1.097108176
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row-99
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07254
86.3%
1741
 
8.8%
-99408
 
4.9%

Length

2022-04-28T12:42:09.159546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:09.237862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
07254
86.3%
1741
 
8.8%
99408
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE3_71
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
7401 
9
 
725
-99
 
231
1
 
46

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
27401
88.1%
9725
 
8.6%
-99231
 
2.7%
146
 
0.5%

Length

2022-04-28T12:42:09.321991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:09.400675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
27401
88.1%
9725
 
8.6%
99231
 
2.7%
146
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE3_72
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.122575271
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:09.472890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile8
Q18
median8
Q38
95-th percentile9
Maximum9
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.51277885
Coefficient of variation (CV)3.418745049
Kurtosis31.37922217
Mean5.122575271
Median Absolute Deviation (MAD)0
Skewness-5.774831956
Sum43045
Variance306.6974229
MonotonicityNot monotonic
2022-04-28T12:42:09.572854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
87401
88.1%
9725
 
8.6%
-99231
 
2.7%
713
 
0.2%
312
 
0.1%
19
 
0.1%
25
 
0.1%
53
 
< 0.1%
62
 
< 0.1%
42
 
< 0.1%
ValueCountFrequency (%)
-99231
 
2.7%
19
 
0.1%
25
 
0.1%
312
 
0.1%
42
 
< 0.1%
53
 
< 0.1%
62
 
< 0.1%
713
 
0.2%
87401
88.1%
9725
 
8.6%
ValueCountFrequency (%)
9725
 
8.6%
87401
88.1%
713
 
0.2%
62
 
< 0.1%
53
 
< 0.1%
42
 
< 0.1%
312
 
0.1%
25
 
0.1%
19
 
0.1%
-99231
 
2.7%

BE3_81
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
7206 
9
725 
1
 
241
-99
 
231

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
27206
85.8%
9725
 
8.6%
1241
 
2.9%
-99231
 
2.7%

Length

2022-04-28T12:42:09.705600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:09.943612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
27206
85.8%
9725
 
8.6%
1241
 
2.9%
99231
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE3_82
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.011900512
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:10.017254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile5
Q18
median8
Q38
95-th percentile9
Maximum9
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.51195331
Coefficient of variation (CV)3.494074407
Kurtosis31.23986883
Mean5.011900512
Median Absolute Deviation (MAD)0
Skewness-5.756879107
Sum42115
Variance306.6685087
MonotonicityNot monotonic
2022-04-28T12:42:10.114806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
87206
85.8%
9729
 
8.7%
-99231
 
2.7%
363
 
0.7%
148
 
0.6%
247
 
0.6%
735
 
0.4%
427
 
0.3%
511
 
0.1%
66
 
0.1%
ValueCountFrequency (%)
-99231
 
2.7%
148
 
0.6%
247
 
0.6%
363
 
0.7%
427
 
0.3%
511
 
0.1%
66
 
0.1%
735
 
0.4%
87206
85.8%
9729
 
8.7%
ValueCountFrequency (%)
9729
 
8.7%
87206
85.8%
735
 
0.4%
66
 
0.1%
511
 
0.1%
427
 
0.3%
363
 
0.7%
247
 
0.6%
148
 
0.6%
-99231
 
2.7%

BE3_75
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
7303 
9
731 
-99
 
231
1
 
138

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
27303
86.9%
9731
 
8.7%
-99231
 
2.7%
1138
 
1.6%

Length

2022-04-28T12:42:10.231530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:10.311163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
27303
86.9%
9731
 
8.7%
99231
 
2.7%
1138
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE3_76
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.081994526
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:10.386378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile8
Q18
median8
Q38
95-th percentile9
Maximum9
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.5113066
Coefficient of variation (CV)3.445754715
Kurtosis31.33724393
Mean5.081994526
Median Absolute Deviation (MAD)0
Skewness-5.769393083
Sum42704
Variance306.6458588
MonotonicityNot monotonic
2022-04-28T12:42:10.484381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
87303
86.9%
9731
 
8.7%
-99231
 
2.7%
527
 
0.3%
326
 
0.3%
723
 
0.3%
421
 
0.2%
221
 
0.2%
610
 
0.1%
110
 
0.1%
ValueCountFrequency (%)
-99231
 
2.7%
110
 
0.1%
221
 
0.2%
326
 
0.3%
421
 
0.2%
527
 
0.3%
610
 
0.1%
723
 
0.3%
87303
86.9%
9731
 
8.7%
ValueCountFrequency (%)
9731
 
8.7%
87303
86.9%
723
 
0.3%
610
 
0.1%
527
 
0.3%
421
 
0.2%
326
 
0.3%
221
 
0.2%
110
 
0.1%
-99231
 
2.7%

BE3_85
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
6468 
1
974 
9
730 
-99
 
231

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
26468
77.0%
1974
 
11.6%
9730
 
8.7%
-99231
 
2.7%

Length

2022-04-28T12:42:10.602892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:10.689306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
26468
77.0%
1974
 
11.6%
9730
 
8.7%
99231
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE3_86
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.695703915
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:10.784568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile2
Q18
median8
Q38
95-th percentile9
Maximum9
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.49557333
Coefficient of variation (CV)3.725868081
Kurtosis30.95260861
Mean4.695703915
Median Absolute Deviation (MAD)0
Skewness-5.71936162
Sum39458
Variance306.0950863
MonotonicityNot monotonic
2022-04-28T12:42:10.892479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
86468
77.0%
9731
 
8.7%
-99231
 
2.7%
3203
 
2.4%
5198
 
2.4%
7168
 
2.0%
2154
 
1.8%
4112
 
1.3%
171
 
0.8%
667
 
0.8%
ValueCountFrequency (%)
-99231
 
2.7%
171
 
0.8%
2154
 
1.8%
3203
 
2.4%
4112
 
1.3%
5198
 
2.4%
667
 
0.8%
7168
 
2.0%
86468
77.0%
9731
 
8.7%
ValueCountFrequency (%)
9731
 
8.7%
86468
77.0%
7168
 
2.0%
667
 
0.8%
5198
 
2.4%
4112
 
1.3%
3203
 
2.4%
2154
 
1.8%
171
 
0.8%
-99231
 
2.7%

BE3_91
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
3766 
1
3678 
9
728 
-99
 
231

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
23766
44.8%
13678
43.8%
9728
 
8.7%
-99231
 
2.7%

Length

2022-04-28T12:42:11.041050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:11.128816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
23766
44.8%
13678
43.8%
9728
 
8.7%
99231
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BE8_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.80911579
Minimum-99
Maximum99
Zeros1
Zeros (%)< 0.1%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:11.215584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile2
Q16
median9
Q313
95-th percentile99
Maximum99
Range198
Interquartile range (IQR)7

Descriptive statistics

Standard deviation35.76884618
Coefficient of variation (CV)2.127943351
Kurtosis3.497690684
Mean16.80911579
Median Absolute Deviation (MAD)4
Skewness0.5591539348
Sum141247
Variance1279.410357
MonotonicityNot monotonic
2022-04-28T12:42:11.330277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
991045
12.4%
10971
11.6%
8769
9.2%
5716
 
8.5%
6666
 
7.9%
12566
 
6.7%
7520
 
6.2%
4497
 
5.9%
9430
 
5.1%
3346
 
4.1%
Other values (15)1877
22.3%
ValueCountFrequency (%)
-99231
 
2.7%
01
 
< 0.1%
150
 
0.6%
2194
 
2.3%
3346
4.1%
4497
5.9%
5716
8.5%
6666
7.9%
7520
6.2%
8769
9.2%
ValueCountFrequency (%)
991045
12.4%
221
 
< 0.1%
212
 
< 0.1%
2011
 
0.1%
196
 
0.1%
1853
 
0.6%
1766
 
0.8%
16107
 
1.3%
15262
 
3.1%
14288
 
3.4%

BE3_31
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.48684993
Minimum-99
Maximum99
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:11.436676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1
Q11
median5
Q38
95-th percentile99
Maximum99
Range198
Interquartile range (IQR)7

Descriptive statistics

Standard deviation33.08521112
Coefficient of variation (CV)3.154923673
Kurtosis5.059663718
Mean10.48684993
Median Absolute Deviation (MAD)3
Skewness0.7729916481
Sum88121
Variance1094.631195
MonotonicityNot monotonic
2022-04-28T12:42:11.584720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
82191
26.1%
11953
23.2%
4860
 
10.2%
99780
 
9.3%
6653
 
7.8%
3585
 
7.0%
5467
 
5.6%
2388
 
4.6%
7295
 
3.5%
-99231
 
2.7%
ValueCountFrequency (%)
-99231
 
2.7%
11953
23.2%
2388
 
4.6%
3585
 
7.0%
4860
 
10.2%
5467
 
5.6%
6653
 
7.8%
7295
 
3.5%
82191
26.1%
99780
 
9.3%
ValueCountFrequency (%)
99780
 
9.3%
82191
26.1%
7295
 
3.5%
6653
 
7.8%
5467
 
5.6%
4860
 
10.2%
3585
 
7.0%
2388
 
4.6%
11953
23.2%
-99231
 
2.7%

BE3_32
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.46697608
Minimum-99
Maximum99
Zeros2858
Zeros (%)34.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:11.724255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile0
Q10
median1
Q388
95-th percentile99
Maximum99
Range198
Interquartile range (IQR)88

Descriptive statistics

Standard deviation47.24376457
Coefficient of variation (CV)1.720020596
Kurtosis-0.4030984517
Mean27.46697608
Median Absolute Deviation (MAD)1
Skewness0.168350014
Sum230805
Variance2231.97329
MonotonicityNot monotonic
2022-04-28T12:42:11.857211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
02858
34.0%
881953
23.2%
11721
20.5%
99785
 
9.3%
2577
 
6.9%
-99231
 
2.7%
3163
 
1.9%
944
 
0.5%
438
 
0.5%
521
 
0.2%
Other values (3)12
 
0.1%
ValueCountFrequency (%)
-99231
 
2.7%
02858
34.0%
11721
20.5%
2577
 
6.9%
3163
 
1.9%
438
 
0.5%
521
 
0.2%
68
 
0.1%
72
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
99785
9.3%
881953
23.2%
944
 
0.5%
82
 
< 0.1%
72
 
< 0.1%
68
 
0.1%
521
 
0.2%
438
 
0.5%
3163
 
1.9%
2577
 
6.9%

BE5_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.382720457
Minimum-99
Maximum9
Zeros0
Zeros (%)0.0%
Negative231
Negative (%)2.7%
Memory size65.8 KiB
2022-04-28T12:42:12.001827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1
Q11
median1
Q32
95-th percentile9
Maximum9
Range108
Interquartile range (IQR)1

Descriptive statistics

Standard deviation16.78084217
Coefficient of variation (CV)-43.84621168
Kurtosis29.82674396
Mean-0.382720457
Median Absolute Deviation (MAD)0
Skewness-5.55928324
Sum-3216
Variance281.596664
MonotonicityNot monotonic
2022-04-28T12:42:12.131482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
16030
71.8%
6762
 
9.1%
9750
 
8.9%
4247
 
2.9%
-99231
 
2.7%
3175
 
2.1%
5124
 
1.5%
284
 
1.0%
ValueCountFrequency (%)
-99231
 
2.7%
16030
71.8%
284
 
1.0%
3175
 
2.1%
4247
 
2.9%
5124
 
1.5%
6762
 
9.1%
9750
 
8.9%
ValueCountFrequency (%)
9750
 
8.9%
6762
 
9.1%
5124
 
1.5%
4247
 
2.9%
3175
 
2.1%
284
 
1.0%
16030
71.8%
-99231
 
2.7%

pa_aerobic
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
5072 
1
2319 
-99
1012 

Length

Max length3
Median length1
Mean length1.240866357
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
05072
60.4%
12319
27.6%
-991012
 
12.0%

Length

2022-04-28T12:42:12.283077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:12.386798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05072
60.4%
12319
27.6%
991012
 
12.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI1_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
4401 
8
3601 
-99
 
231
0
 
166
9
 
4

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row8
4th row8
5th row1

Common Values

ValueCountFrequency (%)
14401
52.4%
83601
42.9%
-99231
 
2.7%
0166
 
2.0%
94
 
< 0.1%

Length

2022-04-28T12:42:12.503516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:12.621171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14401
52.4%
83601
42.9%
99231
 
2.7%
0166
 
2.0%
94
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
5328 
1
2446 
0
 
392
-99
 
231
9
 
6

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row8
4th row1
5th row8

Common Values

ValueCountFrequency (%)
85328
63.4%
12446
29.1%
0392
 
4.7%
-99231
 
2.7%
96
 
0.1%

Length

2022-04-28T12:42:12.758838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:12.863523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
85328
63.4%
12446
29.1%
0392
 
4.7%
99231
 
2.7%
96
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI3_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7034 
9
 
666
1
 
380
-99
 
231
0
 
92

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87034
83.7%
9666
 
7.9%
1380
 
4.5%
-99231
 
2.7%
092
 
1.1%

Length

2022-04-28T12:42:12.969246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:13.058017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87034
83.7%
9666
 
7.9%
1380
 
4.5%
99231
 
2.7%
092
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
6848 
-99
906 
1
 
579
0
 
70

Length

Max length3
Median length1
Mean length1.215637272
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
86848
81.5%
-99906
 
10.8%
1579
 
6.9%
070
 
0.8%

Length

2022-04-28T12:42:13.283202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:13.384961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
86848
81.5%
99906
 
10.8%
1579
 
6.9%
070
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI5_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7249 
9
 
673
1
 
231
-99
 
231
0
 
19

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87249
86.3%
9673
 
8.0%
1231
 
2.7%
-99231
 
2.7%
019
 
0.2%

Length

2022-04-28T12:42:13.477685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:13.557511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87249
86.3%
9673
 
8.0%
99231
 
2.7%
1231
 
2.7%
019
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DM2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
5215 
1
2069 
9
677 
-99
 
231
0
 
211

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
85215
62.1%
12069
 
24.6%
9677
 
8.1%
-99231
 
2.7%
0211
 
2.5%

Length

2022-04-28T12:42:13.651983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:13.734766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
85215
62.1%
12069
 
24.6%
9677
 
8.1%
99231
 
2.7%
0211
 
2.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DM3_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7186 
9
 
677
1
 
250
-99
 
231
0
 
59

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87186
85.5%
9677
 
8.1%
1250
 
3.0%
-99231
 
2.7%
059
 
0.7%

Length

2022-04-28T12:42:13.833177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:13.917974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87186
85.5%
9677
 
8.1%
1250
 
3.0%
99231
 
2.7%
059
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DM4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
5701 
1
1489 
9
680 
0
 
302
-99
 
231

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row1
5th row8

Common Values

ValueCountFrequency (%)
85701
67.8%
11489
 
17.7%
9680
 
8.1%
0302
 
3.6%
-99231
 
2.7%

Length

2022-04-28T12:42:14.016578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:14.100356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
85701
67.8%
11489
 
17.7%
9680
 
8.1%
0302
 
3.6%
99231
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DJ2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7004 
9
 
680
0
 
473
-99
 
231
1
 
15

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87004
83.4%
9680
 
8.1%
0473
 
5.6%
-99231
 
2.7%
115
 
0.2%

Length

2022-04-28T12:42:14.196100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:14.277884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87004
83.4%
9680
 
8.1%
0473
 
5.6%
99231
 
2.7%
115
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DJ4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7134 
9
 
680
1
 
277
-99
 
231
0
 
81

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row1
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87134
84.9%
9680
 
8.1%
1277
 
3.3%
-99231
 
2.7%
081
 
1.0%

Length

2022-04-28T12:42:14.373475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:14.455273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87134
84.9%
9680
 
8.1%
1277
 
3.3%
99231
 
2.7%
081
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DJ6_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7118 
9
 
684
-99
 
231
0
 
223
1
 
147

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87118
84.7%
9684
 
8.1%
-99231
 
2.7%
0223
 
2.7%
1147
 
1.7%

Length

2022-04-28T12:42:14.571952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:14.650749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87118
84.7%
9684
 
8.1%
99231
 
2.7%
0223
 
2.7%
1147
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DJ8_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7001 
9
 
684
1
 
402
-99
 
231
0
 
85

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row1
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87001
83.3%
9684
 
8.1%
1402
 
4.8%
-99231
 
2.7%
085
 
1.0%

Length

2022-04-28T12:42:14.745590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:14.834222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87001
83.3%
9684
 
8.1%
1402
 
4.8%
99231
 
2.7%
085
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DI6_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7044 
9
 
675
1
 
394
-99
 
231
0
 
59

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87044
83.8%
9675
 
8.0%
1394
 
4.7%
-99231
 
2.7%
059
 
0.7%

Length

2022-04-28T12:42:14.935220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:15.014128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87044
83.8%
9675
 
8.0%
1394
 
4.7%
99231
 
2.7%
059
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DF2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
6983 
9
 
684
1
 
330
-99
 
231
0
 
175

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row1
5th row8

Common Values

ValueCountFrequency (%)
86983
83.1%
9684
 
8.1%
1330
 
3.9%
-99231
 
2.7%
0175
 
2.1%

Length

2022-04-28T12:42:15.114291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:15.197074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
86983
83.1%
9684
 
8.1%
1330
 
3.9%
99231
 
2.7%
0175
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DL1_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7393 
9
 
684
-99
 
231
1
 
75
0
 
20

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87393
88.0%
9684
 
8.1%
-99231
 
2.7%
175
 
0.9%
020
 
0.2%

Length

2022-04-28T12:42:15.291382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:15.374361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87393
88.0%
9684
 
8.1%
99231
 
2.7%
175
 
0.9%
020
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DE1_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
6333 
1
1781 
-99
 
231
0
 
50
9
 
8

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row1

Common Values

ValueCountFrequency (%)
86333
75.4%
11781
 
21.2%
-99231
 
2.7%
050
 
0.6%
98
 
0.1%

Length

2022-04-28T12:42:15.477093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:15.561860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
86333
75.4%
11781
 
21.2%
99231
 
2.7%
050
 
0.6%
98
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DE2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7170 
9
 
683
-99
 
231
1
 
202
0
 
117

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87170
85.3%
9683
 
8.1%
-99231
 
2.7%
1202
 
2.4%
0117
 
1.4%

Length

2022-04-28T12:42:15.660598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:15.743377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87170
85.3%
9683
 
8.1%
99231
 
2.7%
1202
 
2.4%
0117
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DH4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7174 
9
 
685
-99
 
231
0
 
223
1
 
90

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87174
85.4%
9685
 
8.2%
-99231
 
2.7%
0223
 
2.7%
190
 
1.1%

Length

2022-04-28T12:42:15.838733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:15.937471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87174
85.4%
9685
 
8.2%
99231
 
2.7%
0223
 
2.7%
190
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC1_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7341 
9
 
683
-99
 
231
0
 
109
1
 
39

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87341
87.4%
9683
 
8.1%
-99231
 
2.7%
0109
 
1.3%
139
 
0.5%

Length

2022-04-28T12:42:16.162762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:16.241539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87341
87.4%
9683
 
8.1%
99231
 
2.7%
0109
 
1.3%
139
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC2_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7470 
9
 
683
-99
 
231
1
 
13
0
 
6

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87470
88.9%
9683
 
8.1%
-99231
 
2.7%
113
 
0.2%
06
 
0.1%

Length

2022-04-28T12:42:16.332297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:16.456964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87470
88.9%
9683
 
8.1%
99231
 
2.7%
113
 
0.2%
06
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC3_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7367 
9
 
683
-99
 
231
0
 
90
1
 
32

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87367
87.7%
9683
 
8.1%
-99231
 
2.7%
090
 
1.1%
132
 
0.4%

Length

2022-04-28T12:42:16.559690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:16.640512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87367
87.7%
9683
 
8.1%
99231
 
2.7%
090
 
1.1%
132
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7406 
9
 
683
-99
 
231
0
 
49
1
 
34

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87406
88.1%
9683
 
8.1%
-99231
 
2.7%
049
 
0.6%
134
 
0.4%

Length

2022-04-28T12:42:16.756201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:16.860885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87406
88.1%
9683
 
8.1%
99231
 
2.7%
049
 
0.6%
134
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC5_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7425 
9
 
683
-99
 
231
0
 
61
1
 
3

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87425
88.4%
9683
 
8.1%
-99231
 
2.7%
061
 
0.7%
13
 
< 0.1%

Length

2022-04-28T12:42:16.979567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:17.104234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87425
88.4%
9683
 
8.1%
99231
 
2.7%
061
 
0.7%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC6_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7439 
9
 
683
-99
 
231
1
 
30
0
 
20

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87439
88.5%
9683
 
8.1%
-99231
 
2.7%
130
 
0.4%
020
 
0.2%

Length

2022-04-28T12:42:17.236774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:17.349826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87439
88.5%
9683
 
8.1%
99231
 
2.7%
130
 
0.4%
020
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DC7_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7396 
9
 
683
-99
 
231
0
 
50
1
 
43

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87396
88.0%
9683
 
8.1%
-99231
 
2.7%
050
 
0.6%
143
 
0.5%

Length

2022-04-28T12:42:17.476489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:17.575649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87396
88.0%
9683
 
8.1%
99231
 
2.7%
050
 
0.6%
143
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DK8_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7373 
9
 
688
-99
 
231
0
 
59
1
 
52

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87373
87.7%
9688
 
8.2%
-99231
 
2.7%
059
 
0.7%
152
 
0.6%

Length

2022-04-28T12:42:17.682364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:17.827975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87373
87.7%
9688
 
8.2%
99231
 
2.7%
059
 
0.7%
152
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DK9_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7456 
9
 
688
-99
 
231
0
 
18
1
 
10

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87456
88.7%
9688
 
8.2%
-99231
 
2.7%
018
 
0.2%
110
 
0.1%

Length

2022-04-28T12:42:17.955667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:18.065341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87456
88.7%
9688
 
8.2%
99231
 
2.7%
018
 
0.2%
110
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DK4_pr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
8
7437 
9
 
688
-99
 
231
1
 
36
0
 
11

Length

Max length3
Median length1
Mean length1.054980364
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
87437
88.5%
9688
 
8.2%
-99231
 
2.7%
136
 
0.4%
011
 
0.1%

Length

2022-04-28T12:42:18.182269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:18.279413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
87437
88.5%
9688
 
8.2%
99231
 
2.7%
136
 
0.4%
011
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_Upro
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.001190051
Minimum-99
Maximum5
Zeros6561
Zeros (%)78.1%
Negative612
Negative (%)7.3%
Memory size65.8 KiB
2022-04-28T12:42:18.365185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q10
median0
Q30
95-th percentile1
Maximum5
Range104
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.79254109
Coefficient of variation (CV)-3.684022417
Kurtosis8.803543237
Mean-7.001190051
Median Absolute Deviation (MAD)0
Skewness-3.285403897
Sum-58831
Variance665.2551759
MonotonicityNot monotonic
2022-04-28T12:42:18.484864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
06561
78.1%
1842
 
10.0%
-99612
 
7.3%
2275
 
3.3%
391
 
1.1%
418
 
0.2%
54
 
< 0.1%
ValueCountFrequency (%)
-99612
 
7.3%
06561
78.1%
1842
 
10.0%
2275
 
3.3%
391
 
1.1%
418
 
0.2%
54
 
< 0.1%
ValueCountFrequency (%)
54
 
< 0.1%
418
 
0.2%
391
 
1.1%
2275
 
3.3%
1842
 
10.0%
06561
78.1%
-99612
 
7.3%

HE_THfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6490 
9
1650 
-99
 
261
1
 
2

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06490
77.2%
91650
 
19.6%
-99261
 
3.1%
12
 
< 0.1%

Length

2022-04-28T12:42:18.644469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:18.750155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06490
77.2%
91650
 
19.6%
99261
 
3.1%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_THfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6719 
9
1387 
-99
 
261
1
 
36

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06719
80.0%
91387
 
16.5%
-99261
 
3.1%
136
 
0.4%

Length

2022-04-28T12:42:18.864438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:18.966151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06719
80.0%
91387
 
16.5%
99261
 
3.1%
136
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_THfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6848 
9
1031 
-99
 
261
8
 
162
1
 
101

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06848
81.5%
91031
 
12.3%
-99261
 
3.1%
8162
 
1.9%
1101
 
1.2%

Length

2022-04-28T12:42:19.087428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:19.196142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06848
81.5%
91031
 
12.3%
99261
 
3.1%
8162
 
1.9%
1101
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HBfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6485 
9
1651 
-99
 
261
1
 
6

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06485
77.2%
91651
 
19.6%
-99261
 
3.1%
16
 
0.1%

Length

2022-04-28T12:42:19.310870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:19.396638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06485
77.2%
91651
 
19.6%
99261
 
3.1%
16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HBfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6742 
9
1389 
-99
 
261
1
 
11

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06742
80.2%
91389
 
16.5%
-99261
 
3.1%
111
 
0.1%

Length

2022-04-28T12:42:19.611031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:19.712759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06742
80.2%
91389
 
16.5%
99261
 
3.1%
111
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HBfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6916 
9
1034 
-99
 
261
8
 
162
1
 
30

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06916
82.3%
91034
 
12.3%
-99261
 
3.1%
8162
 
1.9%
130
 
0.4%

Length

2022-04-28T12:42:19.799884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:19.878697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06916
82.3%
91034
 
12.3%
99261
 
3.1%
8162
 
1.9%
130
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_fh
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
3752 
1
3718 
9
672 
-99
 
261

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
03752
44.7%
13718
44.2%
9672
 
8.0%
-99261
 
3.1%

Length

2022-04-28T12:42:19.972460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:20.049324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
03752
44.7%
13718
44.2%
9672
 
8.0%
99261
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HPfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
5885 
9
1631 
1
626 
-99
 
261

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
05885
70.0%
91631
 
19.4%
1626
 
7.4%
-99261
 
3.1%

Length

2022-04-28T12:42:20.137090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:20.212888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05885
70.0%
91631
 
19.4%
1626
 
7.4%
99261
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HPfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
5772 
9
1358 
1
1012 
-99
 
261

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
05772
68.7%
91358
 
16.2%
11012
 
12.0%
-99261
 
3.1%

Length

2022-04-28T12:42:20.300851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:20.378444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05772
68.7%
91358
 
16.2%
11012
 
12.0%
99261
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HPfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
5523 
1
1480 
9
976 
-99
 
261
8
 
163

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
05523
65.7%
11480
 
17.6%
9976
 
11.6%
-99261
 
3.1%
8163
 
1.9%

Length

2022-04-28T12:42:20.467209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:20.572014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05523
65.7%
11480
 
17.6%
9976
 
11.6%
99261
 
3.1%
8163
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HLfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6455 
9
1666 
-99
 
261
1
 
21

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06455
76.8%
91666
 
19.8%
-99261
 
3.1%
121
 
0.2%

Length

2022-04-28T12:42:20.666370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:20.746159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06455
76.8%
91666
 
19.8%
99261
 
3.1%
121
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HLfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6700 
9
1401 
-99
 
261
1
 
41

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06700
79.7%
91401
 
16.7%
-99261
 
3.1%
141
 
0.5%

Length

2022-04-28T12:42:20.834146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:20.913935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06700
79.7%
91401
 
16.7%
99261
 
3.1%
141
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HLfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6811 
9
1043 
-99
 
261
8
 
163
1
 
125

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
06811
81.1%
91043
 
12.4%
-99261
 
3.1%
8163
 
1.9%
1125
 
1.5%

Length

2022-04-28T12:42:21.003694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:21.084478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06811
81.1%
91043
 
12.4%
99261
 
3.1%
8163
 
1.9%
1125
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_IHDfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6416 
9
1654 
-99
 
261
1
 
72

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06416
76.4%
91654
 
19.7%
-99261
 
3.1%
172
 
0.9%

Length

2022-04-28T12:42:21.182895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:21.277643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06416
76.4%
91654
 
19.7%
99261
 
3.1%
172
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_IHDfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6638 
9
1394 
-99
 
261
1
 
110

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06638
79.0%
91394
 
16.6%
-99261
 
3.1%
1110
 
1.3%

Length

2022-04-28T12:42:21.365397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:21.442186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06638
79.0%
91394
 
16.6%
99261
 
3.1%
1110
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_IHDfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6798 
9
1031 
-99
 
261
8
 
163
1
 
150

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06798
80.9%
91031
 
12.3%
-99261
 
3.1%
8163
 
1.9%
1150
 
1.8%

Length

2022-04-28T12:42:21.532573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:21.614391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06798
80.9%
91031
 
12.3%
99261
 
3.1%
8163
 
1.9%
1150
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_STRfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6015 
9
1592 
1
 
535
-99
 
261

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06015
71.6%
91592
 
18.9%
1535
 
6.4%
-99261
 
3.1%

Length

2022-04-28T12:42:21.732042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:21.808867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06015
71.6%
91592
 
18.9%
1535
 
6.4%
99261
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_STRfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6265 
9
1345 
1
 
532
-99
 
261

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06265
74.6%
91345
 
16.0%
1532
 
6.3%
-99261
 
3.1%

Length

2022-04-28T12:42:21.917402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:21.997190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06265
74.6%
91345
 
16.0%
1532
 
6.3%
99261
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_STRfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6664 
9
1012 
1
 
303
-99
 
261
8
 
163

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
06664
79.3%
91012
 
12.0%
1303
 
3.6%
-99261
 
3.1%
8163
 
1.9%

Length

2022-04-28T12:42:22.101826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:22.181614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06664
79.3%
91012
 
12.0%
1303
 
3.6%
99261
 
3.1%
8163
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_DMfh1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6330 
9
1646 
-99
 
261
1
 
166

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06330
75.3%
91646
 
19.6%
-99261
 
3.1%
1166
 
2.0%

Length

2022-04-28T12:42:22.275393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:22.485197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06330
75.3%
91646
 
19.6%
99261
 
3.1%
1166
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_DMfh2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6382 
9
1370 
1
 
390
-99
 
261

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06382
75.9%
91370
 
16.3%
1390
 
4.6%
-99261
 
3.1%

Length

2022-04-28T12:42:22.577951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:22.654749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06382
75.9%
91370
 
16.3%
1390
 
4.6%
99261
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_DMfh3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6044 
9
990 
1
945 
-99
 
261
8
 
163

Length

Max length3
Median length1
Mean length1.062120671
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
06044
71.9%
9990
 
11.8%
1945
 
11.2%
-99261
 
3.1%
8163
 
1.9%

Length

2022-04-28T12:42:22.746314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:22.824114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06044
71.9%
9990
 
11.8%
1945
 
11.2%
99261
 
3.1%
8163
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
3
5136 
2
1679 
1
1308 
-99
 
280

Length

Max length3
Median length1
Mean length1.066642866
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
35136
61.1%
21679
 
20.0%
11308
 
15.6%
-99280
 
3.3%

Length

2022-04-28T12:42:22.940798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:23.022707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
35136
61.1%
21679
 
20.0%
11308
 
15.6%
99280
 
3.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_obe
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.425800309
Minimum-99
Maximum6
Zeros0
Zeros (%)0.0%
Negative355
Negative (%)4.2%
Memory size65.8 KiB
2022-04-28T12:42:23.097422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum6
Range105
Interquartile range (IQR)2

Descriptive statistics

Standard deviation20.51522297
Coefficient of variation (CV)-14.38856678
Kurtosis18.63805264
Mean-1.425800309
Median Absolute Deviation (MAD)1
Skewness-4.53683748
Sum-11981
Variance420.8743735
MonotonicityNot monotonic
2022-04-28T12:42:23.208122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
23156
37.6%
32276
27.1%
42121
25.2%
-99355
 
4.2%
1239
 
2.8%
5235
 
2.8%
621
 
0.2%
ValueCountFrequency (%)
-99355
 
4.2%
1239
 
2.8%
23156
37.6%
32276
27.1%
42121
25.2%
5235
 
2.8%
621
 
0.2%
ValueCountFrequency (%)
621
 
0.2%
5235
 
2.8%
42121
25.2%
32276
27.1%
23156
37.6%
1239
 
2.8%
-99355
 
4.2%

HE_DM_HbA1c
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
2818 
1
2504 
3
2047 
-99
1034 

Length

Max length3
Median length1
Mean length1.246102582
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
22818
33.5%
12504
29.8%
32047
24.4%
-991034
 
12.3%

Length

2022-04-28T12:42:23.317789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:23.395613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
22818
33.5%
12504
29.8%
32047
24.4%
991034
 
12.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HCHOL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
4726 
1
2645 
-99
1032 

Length

Max length3
Median length1
Mean length1.245626562
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
04726
56.2%
12645
31.5%
-991032
 
12.3%

Length

2022-04-28T12:42:23.482336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:23.584065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04726
56.2%
12645
31.5%
991032
 
12.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_HTG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
5822 
-99
1765 
1
816 

Length

Max length3
Median length1
Mean length1.420088064
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row-99
4th row0
5th row0

Common Values

ValueCountFrequency (%)
05822
69.3%
-991765
 
21.0%
1816
 
9.7%

Length

2022-04-28T12:42:23.688362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:23.764161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05822
69.3%
991765
 
21.0%
1816
 
9.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_hepaB
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
7511 
-99
 
698
1
 
194

Length

Max length3
Median length1
Mean length1.166131144
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07511
89.4%
-99698
 
8.3%
1194
 
2.3%

Length

2022-04-28T12:42:23.873869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:23.957682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
07511
89.4%
99698
 
8.3%
1194
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_hepaC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
7585 
-99
 
698
1
 
120

Length

Max length3
Median length1
Mean length1.166131144
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07585
90.3%
-99698
 
8.3%
1120
 
1.4%

Length

2022-04-28T12:42:24.065390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:24.146180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
07585
90.3%
99698
 
8.3%
1120
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HE_anem
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6434 
1
1250 
-99
719 

Length

Max length3
Median length1
Mean length1.171129359
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
06434
76.6%
11250
 
14.9%
-99719
 
8.6%

Length

2022-04-28T12:42:24.230557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:24.309348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06434
76.6%
11250
 
14.9%
99719
 
8.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

T_NQ_OCP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
6079 
-99
1125 
1
1082 
9
 
117

Length

Max length3
Median length1
Mean length1.267761514
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
26079
72.3%
-991125
 
13.4%
11082
 
12.9%
9117
 
1.4%

Length

2022-04-28T12:42:24.394155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:24.469957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
26079
72.3%
991125
 
13.4%
11082
 
12.9%
9117
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

T_Q_VN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2
5290 
1
1843 
-99
1125 
9
 
117
3
 
28

Length

Max length3
Median length1
Mean length1.267761514
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
25290
63.0%
11843
 
21.9%
-991125
 
13.4%
9117
 
1.4%
328
 
0.3%

Length

2022-04-28T12:42:24.581838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:24.664621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
25290
63.0%
11843
 
21.9%
991125
 
13.4%
9117
 
1.4%
328
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_BR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
7032 
-99
965 
1
 
406

Length

Max length3
Median length1
Mean length1.229679876
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07032
83.7%
-99965
 
11.5%
1406
 
4.8%

Length

2022-04-28T12:42:24.775324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:24.851154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
07032
83.7%
99965
 
11.5%
1406
 
4.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_LN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
6880 
-99
965 
1
 
558

Length

Max length3
Median length1
Mean length1.229679876
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
06880
81.9%
-99965
 
11.5%
1558
 
6.6%

Length

2022-04-28T12:42:24.958424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:25.034138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06880
81.9%
99965
 
11.5%
1558
 
6.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_DN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
0
7069 
-99
965 
1
 
369

Length

Max length3
Median length1
Mean length1.229679876
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07069
84.1%
-99965
 
11.5%
1369
 
4.4%

Length

2022-04-28T12:42:25.137826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:25.213655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
07069
84.1%
99965
 
11.5%
1369
 
4.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_BR_FQ
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
6899 
-99
959 
4
 
226
2
 
211
3
 
108

Length

Max length3
Median length1
Mean length1.228251815
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
16899
82.1%
-99959
 
11.4%
4226
 
2.7%
2211
 
2.5%
3108
 
1.3%

Length

2022-04-28T12:42:25.294437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:25.523420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
16899
82.1%
99959
 
11.4%
4226
 
2.7%
2211
 
2.5%
3108
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_LN_FQ
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
6782 
-99
959 
2
 
316
4
 
213
3
 
133

Length

Max length3
Median length1
Mean length1.228251815
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
16782
80.7%
-99959
 
11.4%
2316
 
3.8%
4213
 
2.5%
3133
 
1.6%

Length

2022-04-28T12:42:25.620196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:25.699982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
16782
80.7%
99959
 
11.4%
2316
 
3.8%
4213
 
2.5%
3133
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_DN_FQ
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
7130 
-99
959 
2
 
211
3
 
61
4
 
42

Length

Max length3
Median length1
Mean length1.228251815
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
17130
84.9%
-99959
 
11.4%
2211
 
2.5%
361
 
0.7%
442
 
0.5%

Length

2022-04-28T12:42:25.792710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:25.870503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
17130
84.9%
99959
 
11.4%
2211
 
2.5%
361
 
0.7%
442
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

L_OUT_FQ
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.461501845
Minimum-99
Maximum7
Zeros0
Zeros (%)0.0%
Negative959
Negative (%)11.4%
Memory size65.8 KiB
2022-04-28T12:42:25.946301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q14
median6
Q36
95-th percentile7
Maximum7
Range106
Interquartile range (IQR)2

Descriptive statistics

Standard deviation33.24211009
Coefficient of variation (CV)-5.14464143
Kurtosis3.874023707
Mean-6.461501845
Median Absolute Deviation (MAD)1
Skewness-2.420402857
Sum-54296
Variance1105.037883
MonotonicityNot monotonic
2022-04-28T12:42:26.036197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
62428
28.9%
51849
22.0%
71808
21.5%
-99959
 
11.4%
4534
 
6.4%
3482
 
5.7%
2251
 
3.0%
192
 
1.1%
ValueCountFrequency (%)
-99959
 
11.4%
192
 
1.1%
2251
 
3.0%
3482
 
5.7%
4534
 
6.4%
51849
22.0%
62428
28.9%
71808
21.5%
ValueCountFrequency (%)
71808
21.5%
62428
28.9%
51849
22.0%
4534
 
6.4%
3482
 
5.7%
2251
 
3.0%
192
 
1.1%
-99959
 
11.4%

LS_1YR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
1
3963 
2
3481 
-99
959 

Length

Max length3
Median length1
Mean length1.228251815
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
13963
47.2%
23481
41.4%
-99959
 
11.4%

Length

2022-04-28T12:42:26.168019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T12:42:26.267763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
13963
47.2%
23481
41.4%
99959
 
11.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-28T12:41:54.204246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:04.805889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:08.897954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:13.048108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:17.936867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:21.877337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:26.430612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:30.443259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:34.516321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:38.486032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:42.516167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:46.701248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:50.873977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:55.121756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:59.254911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:03.447578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:07.695661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:11.893887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:16.006694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:20.203104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:24.370975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:28.600959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:32.798010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:37.210944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:41.533069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:45.760732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:49.846586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:54.387202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:04.985483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:09.046390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:13.225885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:18.062591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:22.030782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:26.581984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:30.596853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:34.660616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:38.625515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:42.829103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:46.844009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:51.035203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:55.265764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:59.400327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:03.600425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:07.849607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:12.046086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:16.162673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:20.364466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:24.523323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:28.747568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:32.981792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:37.365268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:41.687079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:45.917448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:50.015812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:54.538327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:05.140070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:09.178890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:13.389331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:18.221778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:22.173042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:26.726064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:30.733473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:34.793185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:38.759931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:42.968989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:47.064277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:51.185219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:55.414649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:59.547638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:03.748445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:07.995196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:12.191456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:16.302630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:20.506696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:24.663448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:28.890497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:33.134712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:37.517298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:41.829650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:46.062751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:50.165534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:54.695468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:05.288647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:09.329757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:13.558333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:18.361868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:22.309748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:26.889757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:30.879684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:34.930862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:38.904465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-28T12:41:07.356521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:11.616725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:15.720501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:19.915898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:24.078921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:28.151376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:32.486715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:36.903433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:41.226934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:45.478523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:49.572838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:53.840458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:58.179245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:08.758955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:12.894051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:17.664518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:21.716084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:26.279062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:30.305627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:34.366690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:38.348656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:42.374416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:46.567214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:50.725642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:54.980359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:40:59.113008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:03.314337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:07.510409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:11.754601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:15.856418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:20.061995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:24.219958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:28.311435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:32.645594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:37.060681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:41.379225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:45.624325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:49.708934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-28T12:41:53.992503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-04-28T12:42:26.549014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-28T12:42:28.315907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-28T12:42:30.233807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-28T12:42:32.119838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-28T12:42:33.850244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-28T12:41:59.221799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IDyearsexageHE_htHE_wtHE_BMIM_2_yrM_2_rsLQ_1EQLLQ_2EQLLQ_3EQLLQ_4EQLLQ_5EQLBO1_1BO2_1BD1_11BD2_1BD2_31dr_monthBP16_1BP16_2BP1mh_stressBS1_1BS3_1BS3_2BS3_3BS12_47BS8_2BS9_2BS13sm_presntBE3_71BE3_72BE3_81BE3_82BE3_75BE3_76BE3_85BE3_86BE3_91BE8_1BE3_31BE3_32BE5_1pa_aerobicDI1_prDI2_prDI3_prDI4_prDI5_prDM2_prDM3_prDM4_prDJ2_prDJ4_prDJ6_prDJ8_prDI6_prDF2_prDL1_prDE1_prDE2_prDH4_prDC1_prDC2_prDC3_prDC4_prDC5_prDC6_prDC7_prDK8_prDK9_prDK4_prHE_UproHE_THfh1HE_THfh2HE_THfh3HE_HBfh1HE_HBfh2HE_HBfh3HE_fhHE_HPfh1HE_HPfh2HE_HPfh3HE_HLfh1HE_HLfh2HE_HLfh3HE_IHDfh1HE_IHDfh2HE_IHDfh3HE_STRfh1HE_STRfh2HE_STRfh3HE_DMfh1HE_DMfh2HE_DMfh3HE_HPHE_obeHE_DM_HbA1cHE_HCHOLHE_HTGHE_hepaBHE_hepaCHE_anemT_NQ_OCPT_Q_VNL_BRL_LNL_DNL_BR_FQL_LN_FQL_DN_FQL_OUT_FQLS_1YR
0b'A651228902'2016278150522328821121318880480480303888888883202828282811252111188818888088888888888888088000000010100100100000003221010022-99-99-99-99-99-99-99-99
1b'A651417601'201618015561252881111299999-993904209-9999999999999-99282828282106110118888888181888888888888888800000000000000000000000332100002100011151
2b'A652228901'2016166170662228811111145441390390302388888823102828282811040118888888888888888888888888888000000000000000000000002230-990002200011121
3b'A653184701'201627514449231321233348880360360113888888883202828282829918810818888818888818888888888888810000001001001000001001222100011200012172
4b'A653235705'2016279147773512211211151112402403038888888832028282828299188101888888888888881888888888888-990000000000000000000000333000012201012172
5b'A653252701'201627814552242882112114211042042040388888882220282828281860111888888888888888888888888888000000010000000001000003210000122-99-99-99-99-99-99-99-99
6b'A653302001'201616616881282881111114545142042030238888888320282828281103010118888888888888188888888888800000000000000000000000333110002200011142
7b'A653370001'2016175177632099999999141880999999992121388882219999999999999999-99889-99999999999998999999999999-99999999999999999999999912-99-99-99-99-99-992200011171
8b'A653370002'201627115351229999999912188099999999213888888882209999999999999999-99189-9999999999999899999999999900000001001000000000000321000012200011151
9b'A653383601'201626815270302881111131888030024040138888882320282828281128111188881818888888888888888888800000001111000000000000332000002200011151

Last rows

IDyearsexageHE_htHE_wtHE_BMIM_2_yrM_2_rsLQ_1EQLLQ_2EQLLQ_3EQLLQ_4EQLLQ_5EQLBO1_1BO2_1BD1_11BD2_1BD2_31dr_monthBP16_1BP16_2BP1mh_stressBS1_1BS3_1BS3_2BS3_3BS12_47BS8_2BS9_2BS13sm_presntBE3_71BE3_72BE3_81BE3_82BE3_75BE3_76BE3_85BE3_86BE3_91BE8_1BE3_31BE3_32BE5_1pa_aerobicDI1_prDI2_prDI3_prDI4_prDI5_prDM2_prDM3_prDM4_prDJ2_prDJ4_prDJ6_prDJ8_prDI6_prDF2_prDL1_prDE1_prDE2_prDH4_prDC1_prDC2_prDC3_prDC4_prDC5_prDC6_prDC7_prDK8_prDK9_prDK4_prHE_UproHE_THfh1HE_THfh2HE_THfh3HE_HBfh1HE_HBfh2HE_HBfh3HE_fhHE_HPfh1HE_HPfh2HE_HPfh3HE_HLfh1HE_HLfh2HE_HLfh3HE_IHDfh1HE_IHDfh2HE_IHDfh3HE_STRfh1HE_STRfh2HE_STRfh3HE_DMfh1HE_DMfh2HE_DMfh3HE_HPHE_obeHE_DM_HbA1cHE_HCHOLHE_HTGHE_hepaBHE_hepaCHE_anemT_NQ_OCPT_Q_VNL_BRL_LNL_DNL_BR_FQL_LN_FQL_DN_FQL_OUT_FQLS_1YR
8393b'R802410802'202026515754211521232221880661138888888832028282828168050818881818118888188888888888809009001911900900900900123100002200011171
8394b'R802412501'2020174163612228811111126311663023888888232028282828168011188888888888888888888888888800000001000000000000001322000002200011162
8395b'R802412502'2020275149592628821222118880663038888888232028282828168111188881818888888888888888888800000000000000000000000342000002200014162
8396b'R802415902'2020274159682728821221341880773038888888232028282828168010118888888888888880888888888800000001000000000000011341100001200011172
8397b'R803409101'20201661636423288111111422206830388888882320282828282518810818888888888888888888888888800000001000000000010000232100001200011111
8398b'R804176202'2020166163702628812111114221884038888888832028282828278260118881888888888888888888888809009000900900900900900342100002200011161
8399b'R804205101'20201751636926288111111231115521388888882320282828281104011088888888888888180888888888800000000000000000000000243010002100011161
8400b'R804205102'20202651636123288111311188807104038888888831028282828252210818888888888888888888888888800000000000000000000000132100002200011161
8401b'R804344501'20202651555924288111111121108821388888882320282828282102010888888880080888888888888888800100001010000000000000233010002200011341
8402b'R804371701'2020165171762528811111114331773023888888832028282828148460818888888888888818888888888800000001000000000000110242100001200011161